From 6e89a5c997aa3a26c2faaff78ce77ad17794174c Mon Sep 17 00:00:00 2001
From: roger-creus <creus99@gmail.com>
Date: Wed, 17 Jul 2024 12:32:26 -0400
Subject: [PATCH 1/8] wip

---
 cleanrl/dqn.py       |   4 +-
 cleanrl/pqn.py       | 277 ++++++++++++++++++++++++++++++++++++++++++
 cleanrl/pqn_atari.py | 279 +++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 559 insertions(+), 1 deletion(-)
 create mode 100644 cleanrl/pqn.py
 create mode 100644 cleanrl/pqn_atari.py

diff --git a/cleanrl/dqn.py b/cleanrl/dqn.py
index e74e289c3..7ca633a44 100644
--- a/cleanrl/dqn.py
+++ b/cleanrl/dqn.py
@@ -69,6 +69,8 @@ class Args:
     """timestep to start learning"""
     train_frequency: int = 10
     """the frequency of training"""
+    rew_scale: float = 0.1
+    """the reward scaling factor"""
 
 
 def make_env(env_id, seed, idx, capture_video, run_name):
@@ -193,7 +195,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
         for idx, trunc in enumerate(truncations):
             if trunc:
                 real_next_obs[idx] = infos["final_observation"][idx]
-        rb.add(obs, real_next_obs, actions, rewards, terminations, infos)
+        rb.add(obs, real_next_obs, actions, rewards * args.rew_scale, terminations, infos)
 
         # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
         obs = next_obs
diff --git a/cleanrl/pqn.py b/cleanrl/pqn.py
new file mode 100644
index 000000000..4814e6ba1
--- /dev/null
+++ b/cleanrl/pqn.py
@@ -0,0 +1,277 @@
+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/dqn/#dqnpy
+import os
+import random
+import time
+from dataclasses import dataclass
+
+import envpool
+import gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import tyro
+from stable_baselines3.common.buffers import ReplayBuffer
+from torch.utils.tensorboard import SummaryWriter
+from collections import deque
+from IPython import embed
+
+@dataclass
+class Args:
+    exp_name: str = os.path.basename(__file__)[: -len(".py")]
+    """the name of this experiment"""
+    seed: int = 1
+    """seed of the experiment"""
+    torch_deterministic: bool = True
+    """if toggled, `torch.backends.cudnn.deterministic=False`"""
+    cuda: bool = True
+    """if toggled, cuda will be enabled by default"""
+    track: bool = False
+    """if toggled, this experiment will be tracked with Weights and Biases"""
+    wandb_project_name: str = "cleanRL"
+    """the wandb's project name"""
+    wandb_entity: str = None
+    """the entity (team) of wandb's project"""
+    capture_video: bool = False
+    """whether to capture videos of the agent performances (check out `videos` folder)"""
+    
+    # Algorithm specific arguments
+    env_id: str = "CartPole-v1"
+    """the id of the environment"""
+    total_timesteps: int = 500000
+    """total timesteps of the experiments"""
+    learning_rate: float = 2.5e-4
+    """the learning rate of the optimizer"""
+    num_envs: int = 32
+    """the number of parallel game environments"""
+    num_steps: int = 64
+    """the number of steps to run for each environment per update"""
+    num_minibatches: int = 16
+    """the number of mini-batches"""
+    update_epochs: int = 2
+    """the K epochs to update the policy"""
+    anneal_lr: bool = True
+    """Toggle learning rate annealing"""
+    gamma: float = 0.99
+    """the discount factor gamma"""
+    start_e: float = 1
+    """the starting epsilon for exploration"""
+    end_e: float = 0.05
+    """the ending epsilon for exploration"""
+    exploration_fraction: float = 0.5
+    """the fraction of `total_timesteps` it takes from start_e to end_e"""
+    max_grad_norm: float = 10.0
+    """the maximum norm for the gradient clipping"""
+    rew_scale: float = 0.1
+    """the reward scaling factor"""
+    q_lambda: float = 0.65
+    """the lambda for Q(lambda)"""
+    
+class RecordEpisodeStatistics(gym.Wrapper):
+    def __init__(self, env, deque_size=100):
+        super().__init__(env)
+        self.num_envs = getattr(env, "num_envs", 1)
+        self.episode_returns = None
+        self.episode_lengths = None
+
+    def reset(self, **kwargs):
+        observations = super().reset(**kwargs)
+        self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)
+        self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
+        self.lives = np.zeros(self.num_envs, dtype=np.int32)
+        self.returned_episode_returns = np.zeros(self.num_envs, dtype=np.float32)
+        self.returned_episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
+        return observations
+
+    def step(self, action):
+        observations, rewards, dones, infos = super().step(action)
+        self.episode_returns += rewards
+        self.episode_lengths += 1
+        self.returned_episode_returns[:] = self.episode_returns
+        self.returned_episode_lengths[:] = self.episode_lengths
+        self.episode_returns *= 1 - dones
+        self.episode_lengths *= 1 - dones
+        infos["r"] = self.returned_episode_returns
+        infos["l"] = self.returned_episode_lengths
+        return (
+            observations,
+            rewards,
+            dones,
+            infos,
+        )
+
+# ALGO LOGIC: initialize agent here:
+class QNetwork(nn.Module):
+    def __init__(self, env, norm_type="layer_norm", norm_input=False):
+        super().__init__()
+        
+        # wether to normalize the input or not
+        if norm_input:
+            self.norm_in = nn.BatchNorm1d(np.array(env.single_observation_space.shape).prod())
+            
+        # wether to use layer norm or batch norm for internal layers
+        if norm_type == "layer_norm":
+            self.norm = nn.LayerNorm
+        elif norm_type == "batch_norm":
+            self.norm = nn.BatchNorm1d
+        else:
+            self.norm = lambda x: x
+        
+        self.network = nn.Sequential(
+            nn.Linear(np.array(env.single_observation_space.shape).prod(), 120),
+            self.norm(120),
+            nn.ReLU(),
+            nn.Linear(120, 84),
+            self.norm(84),
+            nn.ReLU(),
+            nn.Linear(84, env.single_action_space.n),
+        )
+
+    def forward(self, x):
+        return self.network(x)
+
+
+def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
+    slope = (end_e - start_e) / duration
+    return max(slope * t + start_e, end_e)
+
+if __name__ == "__main__":
+    args = tyro.cli(Args)
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    args.num_iterations = args.total_timesteps // args.batch_size
+    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer = SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
+    )
+
+    # TRY NOT TO MODIFY: seeding
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    # env setup
+    envs = envpool.make(
+        args.env_id,
+        env_type="gym",
+        num_envs=args.num_envs,
+        seed=args.seed,
+    )
+    envs.num_envs = args.num_envs
+    envs.single_action_space = envs.action_space
+    envs.single_observation_space = envs.observation_space
+    envs = RecordEpisodeStatistics(envs)
+    assert isinstance(envs.action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    # agent setup
+    q_network = QNetwork(envs).to(device)
+    optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate)
+
+    # storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    avg_returns = deque(maxlen=20)
+    
+    global_step = 0
+    start_time = time.time()
+
+    # TRY NOT TO MODIFY: start the game
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    
+    for iteration in range(1, args.num_iterations + 1):
+        # Annealing the rate if instructed to do so.
+        if args.anneal_lr:
+            frac = 1.0 - (iteration - 1.0) / args.num_iterations
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"] = lrnow
+
+        for step in range(0, args.num_steps):
+            global_step += args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done           
+
+            epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
+            if random.random() < epsilon:
+                action = torch.randint(0, envs.single_action_space.n, (envs.num_envs,))
+            else:
+                q_values = q_network(next_obs)
+                action = torch.argmax(q_values, dim=1)
+            actions[step] = action
+
+            # TRY NOT TO MODIFY: execute the game and log data.
+            next_obs, reward, next_done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1) * args.rew_scale
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)
+            
+            # TRY NOT TO MODIFY: record rewards for plotting purposes
+            for idx, d in enumerate(next_done):
+                if d:
+                    print(f"global_step={global_step}, episodic_return={info['r'][idx]}")
+                    avg_returns.append(info["r"][idx])
+                    writer.add_scalar("charts/avg_episodic_return", np.average(avg_returns), global_step)
+                    writer.add_scalar("charts/episodic_return", info["r"][idx], global_step)
+                    writer.add_scalar("charts/episodic_length", info["l"][idx], global_step)
+
+        # bootstrap value if not done
+        with torch.no_grad():
+            returns = torch.zeros_like(rewards).to(device)
+            for t in reversed(range(args.num_steps)):
+                if t == args.num_steps - 1:
+                    next_value, _ = torch.max(q_network(next_obs), dim=-1)
+                    nextnonterminal = 1.0 - next_done
+                    returns[t] = rewards[t] + args.gamma * next_value * nextnonterminal
+                else:
+                    nextnonterminal = 1.0 - dones[t + 1]
+                    next_value, _ = torch.max(q_network(obs[t + 1]), dim=-1)
+                    returns[t] = rewards[t] + args.gamma * (args.q_lambda * returns[t+1] + (1-args.q_lambda) * next_value * nextnonterminal)
+
+        # flatten the batch
+        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+        b_returns = returns.reshape(-1)
+
+        # Optimizing the Q-network
+        b_inds = np.arange(args.batch_size)
+        for epoch in range(args.update_epochs):
+            np.random.shuffle(b_inds)
+            for start in range(0, args.batch_size, args.minibatch_size):
+                end = start + args.minibatch_size
+                mb_inds = b_inds[start:end]
+
+                old_val = q_network(b_obs[mb_inds]).gather(1, b_actions[mb_inds].unsqueeze(-1).long()).squeeze()
+                loss = F.mse_loss(b_returns[mb_inds], old_val)
+
+                # optimize the model
+                optimizer.zero_grad()
+                loss.backward()
+                nn.utils.clip_grad_norm_(q_network.parameters(), args.max_grad_norm)
+                optimizer.step()
+
+        writer.add_scalar("losses/td_loss", loss, global_step)
+        writer.add_scalar("losses/q_values", old_val.mean().item(), global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
+
+    envs.close()
+    writer.close()
diff --git a/cleanrl/pqn_atari.py b/cleanrl/pqn_atari.py
new file mode 100644
index 000000000..a23b84391
--- /dev/null
+++ b/cleanrl/pqn_atari.py
@@ -0,0 +1,279 @@
+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/dqn/#dqn_ataripy
+import os
+import random
+import time
+from dataclasses import dataclass
+
+import gymnasium as gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import tyro
+from stable_baselines3.common.atari_wrappers import (
+    ClipRewardEnv,
+    EpisodicLifeEnv,
+    FireResetEnv,
+    MaxAndSkipEnv,
+    NoopResetEnv,
+)
+from stable_baselines3.common.buffers import ReplayBuffer
+from torch.utils.tensorboard import SummaryWriter
+
+
+@dataclass
+class Args:
+    exp_name: str = os.path.basename(__file__)[: -len(".py")]
+    """the name of this experiment"""
+    seed: int = 1
+    """seed of the experiment"""
+    torch_deterministic: bool = True
+    """if toggled, `torch.backends.cudnn.deterministic=False`"""
+    cuda: bool = True
+    """if toggled, cuda will be enabled by default"""
+    track: bool = False
+    """if toggled, this experiment will be tracked with Weights and Biases"""
+    wandb_project_name: str = "cleanRL"
+    """the wandb's project name"""
+    wandb_entity: str = None
+    """the entity (team) of wandb's project"""
+    capture_video: bool = False
+    """whether to capture videos of the agent performances (check out `videos` folder)"""
+    save_model: bool = False
+    """whether to save model into the `runs/{run_name}` folder"""
+    upload_model: bool = False
+    """whether to upload the saved model to huggingface"""
+    hf_entity: str = ""
+    """the user or org name of the model repository from the Hugging Face Hub"""
+
+    # Algorithm specific arguments
+    env_id: str = "BreakoutNoFrameskip-v4"
+    """the id of the environment"""
+    total_timesteps: int = 10000000
+    """total timesteps of the experiments"""
+    learning_rate: float = 1e-4
+    """the learning rate of the optimizer"""
+    num_envs: int = 1
+    """the number of parallel game environments"""
+    buffer_size: int = 1000000
+    """the replay memory buffer size"""
+    gamma: float = 0.99
+    """the discount factor gamma"""
+    tau: float = 1.0
+    """the target network update rate"""
+    target_network_frequency: int = 1000
+    """the timesteps it takes to update the target network"""
+    batch_size: int = 32
+    """the batch size of sample from the reply memory"""
+    start_e: float = 1
+    """the starting epsilon for exploration"""
+    end_e: float = 0.01
+    """the ending epsilon for exploration"""
+    exploration_fraction: float = 0.10
+    """the fraction of `total-timesteps` it takes from start-e to go end-e"""
+    learning_starts: int = 80000
+    """timestep to start learning"""
+    train_frequency: int = 4
+    """the frequency of training"""
+
+
+def make_env(env_id, seed, idx, capture_video, run_name):
+    def thunk():
+        if capture_video and idx == 0:
+            env = gym.make(env_id, render_mode="rgb_array")
+            env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
+        else:
+            env = gym.make(env_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+
+        env = NoopResetEnv(env, noop_max=30)
+        env = MaxAndSkipEnv(env, skip=4)
+        env = EpisodicLifeEnv(env)
+        if "FIRE" in env.unwrapped.get_action_meanings():
+            env = FireResetEnv(env)
+        env = ClipRewardEnv(env)
+        env = gym.wrappers.ResizeObservation(env, (84, 84))
+        env = gym.wrappers.GrayScaleObservation(env)
+        env = gym.wrappers.FrameStack(env, 4)
+
+        env.action_space.seed(seed)
+        return env
+
+    return thunk
+
+
+# ALGO LOGIC: initialize agent here:
+class QNetwork(nn.Module):
+    def __init__(self, env):
+        super().__init__()
+        self.network = nn.Sequential(
+            nn.Conv2d(4, 32, 8, stride=4),
+            nn.ReLU(),
+            nn.Conv2d(32, 64, 4, stride=2),
+            nn.ReLU(),
+            nn.Conv2d(64, 64, 3, stride=1),
+            nn.ReLU(),
+            nn.Flatten(),
+            nn.Linear(3136, 512),
+            nn.ReLU(),
+            nn.Linear(512, env.single_action_space.n),
+        )
+
+    def forward(self, x):
+        return self.network(x / 255.0)
+
+
+def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
+    slope = (end_e - start_e) / duration
+    return max(slope * t + start_e, end_e)
+
+
+if __name__ == "__main__":
+    import stable_baselines3 as sb3
+
+    if sb3.__version__ < "2.0":
+        raise ValueError(
+            """Ongoing migration: run the following command to install the new dependencies:
+
+poetry run pip install "stable_baselines3==2.0.0a1" "gymnasium[atari,accept-rom-license]==0.28.1"  "ale-py==0.8.1" 
+"""
+        )
+    args = tyro.cli(Args)
+    assert args.num_envs == 1, "vectorized envs are not supported at the moment"
+    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer = SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
+    )
+
+    # TRY NOT TO MODIFY: seeding
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    # env setup
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
+    )
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    q_network = QNetwork(envs).to(device)
+    optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate)
+    target_network = QNetwork(envs).to(device)
+    target_network.load_state_dict(q_network.state_dict())
+
+    rb = ReplayBuffer(
+        args.buffer_size,
+        envs.single_observation_space,
+        envs.single_action_space,
+        device,
+        optimize_memory_usage=True,
+        handle_timeout_termination=False,
+    )
+    start_time = time.time()
+
+    # TRY NOT TO MODIFY: start the game
+    obs, _ = envs.reset(seed=args.seed)
+    for global_step in range(args.total_timesteps):
+        # ALGO LOGIC: put action logic here
+        epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
+        if random.random() < epsilon:
+            actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
+        else:
+            q_values = q_network(torch.Tensor(obs).to(device))
+            actions = torch.argmax(q_values, dim=1).cpu().numpy()
+
+        # TRY NOT TO MODIFY: execute the game and log data.
+        next_obs, rewards, terminations, truncations, infos = envs.step(actions)
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        if "final_info" in infos:
+            for info in infos["final_info"]:
+                if info and "episode" in info:
+                    print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
+                    writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
+                    writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
+
+        # TRY NOT TO MODIFY: save data to reply buffer; handle `final_observation`
+        real_next_obs = next_obs.copy()
+        for idx, trunc in enumerate(truncations):
+            if trunc:
+                real_next_obs[idx] = infos["final_observation"][idx]
+        rb.add(obs, real_next_obs, actions, rewards, terminations, infos)
+
+        # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
+        obs = next_obs
+
+        # ALGO LOGIC: training.
+        if global_step > args.learning_starts:
+            if global_step % args.train_frequency == 0:
+                data = rb.sample(args.batch_size)
+                with torch.no_grad():
+                    target_max, _ = target_network(data.next_observations).max(dim=1)
+                    td_target = data.rewards.flatten() + args.gamma * target_max * (1 - data.dones.flatten())
+                old_val = q_network(data.observations).gather(1, data.actions).squeeze()
+                loss = F.mse_loss(td_target, old_val)
+
+                if global_step % 100 == 0:
+                    writer.add_scalar("losses/td_loss", loss, global_step)
+                    writer.add_scalar("losses/q_values", old_val.mean().item(), global_step)
+                    print("SPS:", int(global_step / (time.time() - start_time)))
+                    writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
+
+                # optimize the model
+                optimizer.zero_grad()
+                loss.backward()
+                optimizer.step()
+
+            # update target network
+            if global_step % args.target_network_frequency == 0:
+                for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()):
+                    target_network_param.data.copy_(
+                        args.tau * q_network_param.data + (1.0 - args.tau) * target_network_param.data
+                    )
+
+    if args.save_model:
+        model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
+        torch.save(q_network.state_dict(), model_path)
+        print(f"model saved to {model_path}")
+        from cleanrl_utils.evals.dqn_eval import evaluate
+
+        episodic_returns = evaluate(
+            model_path,
+            make_env,
+            args.env_id,
+            eval_episodes=10,
+            run_name=f"{run_name}-eval",
+            Model=QNetwork,
+            device=device,
+            epsilon=0.05,
+        )
+        for idx, episodic_return in enumerate(episodic_returns):
+            writer.add_scalar("eval/episodic_return", episodic_return, idx)
+
+        if args.upload_model:
+            from cleanrl_utils.huggingface import push_to_hub
+
+            repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
+            repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
+            push_to_hub(args, episodic_returns, repo_id, "DQN", f"runs/{run_name}", f"videos/{run_name}-eval")
+
+    envs.close()
+    writer.close()

From e9f158f4c825d6ddb21cd233f2a2efa4ef58e4df Mon Sep 17 00:00:00 2001
From: roger-creus <creus99@gmail.com>
Date: Wed, 17 Jul 2024 12:45:30 -0400
Subject: [PATCH 2/8] wip

---
 cleanrl/pqn.py               |  26 +---
 cleanrl/pqn_atari.py         | 279 -----------------------------------
 cleanrl/pqn_atari_envpool.py | 275 ++++++++++++++++++++++++++++++++++
 3 files changed, 282 insertions(+), 298 deletions(-)
 delete mode 100644 cleanrl/pqn_atari.py
 create mode 100644 cleanrl/pqn_atari_envpool.py

diff --git a/cleanrl/pqn.py b/cleanrl/pqn.py
index 4814e6ba1..1b9b3b7bc 100644
--- a/cleanrl/pqn.py
+++ b/cleanrl/pqn.py
@@ -15,7 +15,6 @@
 from stable_baselines3.common.buffers import ReplayBuffer
 from torch.utils.tensorboard import SummaryWriter
 from collections import deque
-from IPython import embed
 
 @dataclass
 class Args:
@@ -103,27 +102,15 @@ def step(self, action):
 
 # ALGO LOGIC: initialize agent here:
 class QNetwork(nn.Module):
-    def __init__(self, env, norm_type="layer_norm", norm_input=False):
+    def __init__(self, env):
         super().__init__()
         
-        # wether to normalize the input or not
-        if norm_input:
-            self.norm_in = nn.BatchNorm1d(np.array(env.single_observation_space.shape).prod())
-            
-        # wether to use layer norm or batch norm for internal layers
-        if norm_type == "layer_norm":
-            self.norm = nn.LayerNorm
-        elif norm_type == "batch_norm":
-            self.norm = nn.BatchNorm1d
-        else:
-            self.norm = lambda x: x
-        
         self.network = nn.Sequential(
             nn.Linear(np.array(env.single_observation_space.shape).prod(), 120),
-            self.norm(120),
+            nn.LayerNorm(120),
             nn.ReLU(),
             nn.Linear(120, 84),
-            self.norm(84),
+            nn.LayerNorm(84),
             nn.ReLU(),
             nn.Linear(84, env.single_action_space.n),
         )
@@ -215,8 +202,9 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
             if random.random() < epsilon:
                 action = torch.randint(0, envs.single_action_space.n, (envs.num_envs,))
             else:
-                q_values = q_network(next_obs)
-                action = torch.argmax(q_values, dim=1)
+                with torch.no_grad():
+                    q_values = q_network(next_obs)
+                    action = torch.argmax(q_values, dim=1)
             actions[step] = action
 
             # TRY NOT TO MODIFY: execute the game and log data.
@@ -233,7 +221,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
                     writer.add_scalar("charts/episodic_return", info["r"][idx], global_step)
                     writer.add_scalar("charts/episodic_length", info["l"][idx], global_step)
 
-        # bootstrap value if not done
+        # Compute Q(lambda) targets
         with torch.no_grad():
             returns = torch.zeros_like(rewards).to(device)
             for t in reversed(range(args.num_steps)):
diff --git a/cleanrl/pqn_atari.py b/cleanrl/pqn_atari.py
deleted file mode 100644
index a23b84391..000000000
--- a/cleanrl/pqn_atari.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/dqn/#dqn_ataripy
-import os
-import random
-import time
-from dataclasses import dataclass
-
-import gymnasium as gym
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-import tyro
-from stable_baselines3.common.atari_wrappers import (
-    ClipRewardEnv,
-    EpisodicLifeEnv,
-    FireResetEnv,
-    MaxAndSkipEnv,
-    NoopResetEnv,
-)
-from stable_baselines3.common.buffers import ReplayBuffer
-from torch.utils.tensorboard import SummaryWriter
-
-
-@dataclass
-class Args:
-    exp_name: str = os.path.basename(__file__)[: -len(".py")]
-    """the name of this experiment"""
-    seed: int = 1
-    """seed of the experiment"""
-    torch_deterministic: bool = True
-    """if toggled, `torch.backends.cudnn.deterministic=False`"""
-    cuda: bool = True
-    """if toggled, cuda will be enabled by default"""
-    track: bool = False
-    """if toggled, this experiment will be tracked with Weights and Biases"""
-    wandb_project_name: str = "cleanRL"
-    """the wandb's project name"""
-    wandb_entity: str = None
-    """the entity (team) of wandb's project"""
-    capture_video: bool = False
-    """whether to capture videos of the agent performances (check out `videos` folder)"""
-    save_model: bool = False
-    """whether to save model into the `runs/{run_name}` folder"""
-    upload_model: bool = False
-    """whether to upload the saved model to huggingface"""
-    hf_entity: str = ""
-    """the user or org name of the model repository from the Hugging Face Hub"""
-
-    # Algorithm specific arguments
-    env_id: str = "BreakoutNoFrameskip-v4"
-    """the id of the environment"""
-    total_timesteps: int = 10000000
-    """total timesteps of the experiments"""
-    learning_rate: float = 1e-4
-    """the learning rate of the optimizer"""
-    num_envs: int = 1
-    """the number of parallel game environments"""
-    buffer_size: int = 1000000
-    """the replay memory buffer size"""
-    gamma: float = 0.99
-    """the discount factor gamma"""
-    tau: float = 1.0
-    """the target network update rate"""
-    target_network_frequency: int = 1000
-    """the timesteps it takes to update the target network"""
-    batch_size: int = 32
-    """the batch size of sample from the reply memory"""
-    start_e: float = 1
-    """the starting epsilon for exploration"""
-    end_e: float = 0.01
-    """the ending epsilon for exploration"""
-    exploration_fraction: float = 0.10
-    """the fraction of `total-timesteps` it takes from start-e to go end-e"""
-    learning_starts: int = 80000
-    """timestep to start learning"""
-    train_frequency: int = 4
-    """the frequency of training"""
-
-
-def make_env(env_id, seed, idx, capture_video, run_name):
-    def thunk():
-        if capture_video and idx == 0:
-            env = gym.make(env_id, render_mode="rgb_array")
-            env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
-        else:
-            env = gym.make(env_id)
-        env = gym.wrappers.RecordEpisodeStatistics(env)
-
-        env = NoopResetEnv(env, noop_max=30)
-        env = MaxAndSkipEnv(env, skip=4)
-        env = EpisodicLifeEnv(env)
-        if "FIRE" in env.unwrapped.get_action_meanings():
-            env = FireResetEnv(env)
-        env = ClipRewardEnv(env)
-        env = gym.wrappers.ResizeObservation(env, (84, 84))
-        env = gym.wrappers.GrayScaleObservation(env)
-        env = gym.wrappers.FrameStack(env, 4)
-
-        env.action_space.seed(seed)
-        return env
-
-    return thunk
-
-
-# ALGO LOGIC: initialize agent here:
-class QNetwork(nn.Module):
-    def __init__(self, env):
-        super().__init__()
-        self.network = nn.Sequential(
-            nn.Conv2d(4, 32, 8, stride=4),
-            nn.ReLU(),
-            nn.Conv2d(32, 64, 4, stride=2),
-            nn.ReLU(),
-            nn.Conv2d(64, 64, 3, stride=1),
-            nn.ReLU(),
-            nn.Flatten(),
-            nn.Linear(3136, 512),
-            nn.ReLU(),
-            nn.Linear(512, env.single_action_space.n),
-        )
-
-    def forward(self, x):
-        return self.network(x / 255.0)
-
-
-def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
-    slope = (end_e - start_e) / duration
-    return max(slope * t + start_e, end_e)
-
-
-if __name__ == "__main__":
-    import stable_baselines3 as sb3
-
-    if sb3.__version__ < "2.0":
-        raise ValueError(
-            """Ongoing migration: run the following command to install the new dependencies:
-
-poetry run pip install "stable_baselines3==2.0.0a1" "gymnasium[atari,accept-rom-license]==0.28.1"  "ale-py==0.8.1" 
-"""
-        )
-    args = tyro.cli(Args)
-    assert args.num_envs == 1, "vectorized envs are not supported at the moment"
-    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
-    if args.track:
-        import wandb
-
-        wandb.init(
-            project=args.wandb_project_name,
-            entity=args.wandb_entity,
-            sync_tensorboard=True,
-            config=vars(args),
-            name=run_name,
-            monitor_gym=True,
-            save_code=True,
-        )
-    writer = SummaryWriter(f"runs/{run_name}")
-    writer.add_text(
-        "hyperparameters",
-        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
-    )
-
-    # TRY NOT TO MODIFY: seeding
-    random.seed(args.seed)
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    torch.backends.cudnn.deterministic = args.torch_deterministic
-
-    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
-
-    # env setup
-    envs = gym.vector.SyncVectorEnv(
-        [make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
-    )
-    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
-
-    q_network = QNetwork(envs).to(device)
-    optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate)
-    target_network = QNetwork(envs).to(device)
-    target_network.load_state_dict(q_network.state_dict())
-
-    rb = ReplayBuffer(
-        args.buffer_size,
-        envs.single_observation_space,
-        envs.single_action_space,
-        device,
-        optimize_memory_usage=True,
-        handle_timeout_termination=False,
-    )
-    start_time = time.time()
-
-    # TRY NOT TO MODIFY: start the game
-    obs, _ = envs.reset(seed=args.seed)
-    for global_step in range(args.total_timesteps):
-        # ALGO LOGIC: put action logic here
-        epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
-        if random.random() < epsilon:
-            actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
-        else:
-            q_values = q_network(torch.Tensor(obs).to(device))
-            actions = torch.argmax(q_values, dim=1).cpu().numpy()
-
-        # TRY NOT TO MODIFY: execute the game and log data.
-        next_obs, rewards, terminations, truncations, infos = envs.step(actions)
-
-        # TRY NOT TO MODIFY: record rewards for plotting purposes
-        if "final_info" in infos:
-            for info in infos["final_info"]:
-                if info and "episode" in info:
-                    print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
-                    writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
-                    writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
-
-        # TRY NOT TO MODIFY: save data to reply buffer; handle `final_observation`
-        real_next_obs = next_obs.copy()
-        for idx, trunc in enumerate(truncations):
-            if trunc:
-                real_next_obs[idx] = infos["final_observation"][idx]
-        rb.add(obs, real_next_obs, actions, rewards, terminations, infos)
-
-        # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
-        obs = next_obs
-
-        # ALGO LOGIC: training.
-        if global_step > args.learning_starts:
-            if global_step % args.train_frequency == 0:
-                data = rb.sample(args.batch_size)
-                with torch.no_grad():
-                    target_max, _ = target_network(data.next_observations).max(dim=1)
-                    td_target = data.rewards.flatten() + args.gamma * target_max * (1 - data.dones.flatten())
-                old_val = q_network(data.observations).gather(1, data.actions).squeeze()
-                loss = F.mse_loss(td_target, old_val)
-
-                if global_step % 100 == 0:
-                    writer.add_scalar("losses/td_loss", loss, global_step)
-                    writer.add_scalar("losses/q_values", old_val.mean().item(), global_step)
-                    print("SPS:", int(global_step / (time.time() - start_time)))
-                    writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
-
-                # optimize the model
-                optimizer.zero_grad()
-                loss.backward()
-                optimizer.step()
-
-            # update target network
-            if global_step % args.target_network_frequency == 0:
-                for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()):
-                    target_network_param.data.copy_(
-                        args.tau * q_network_param.data + (1.0 - args.tau) * target_network_param.data
-                    )
-
-    if args.save_model:
-        model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
-        torch.save(q_network.state_dict(), model_path)
-        print(f"model saved to {model_path}")
-        from cleanrl_utils.evals.dqn_eval import evaluate
-
-        episodic_returns = evaluate(
-            model_path,
-            make_env,
-            args.env_id,
-            eval_episodes=10,
-            run_name=f"{run_name}-eval",
-            Model=QNetwork,
-            device=device,
-            epsilon=0.05,
-        )
-        for idx, episodic_return in enumerate(episodic_returns):
-            writer.add_scalar("eval/episodic_return", episodic_return, idx)
-
-        if args.upload_model:
-            from cleanrl_utils.huggingface import push_to_hub
-
-            repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
-            repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
-            push_to_hub(args, episodic_returns, repo_id, "DQN", f"runs/{run_name}", f"videos/{run_name}-eval")
-
-    envs.close()
-    writer.close()
diff --git a/cleanrl/pqn_atari_envpool.py b/cleanrl/pqn_atari_envpool.py
new file mode 100644
index 000000000..e8ddc8e8e
--- /dev/null
+++ b/cleanrl/pqn_atari_envpool.py
@@ -0,0 +1,275 @@
+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_envpoolpy
+import os
+import random
+import time
+from collections import deque
+from dataclasses import dataclass
+
+import envpool
+import gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import tyro
+from torch.utils.tensorboard import SummaryWriter
+import torch.nn.functional as F
+
+@dataclass
+class Args:
+    exp_name: str = os.path.basename(__file__)[: -len(".py")]
+    """the name of this experiment"""
+    seed: int = 1
+    """seed of the experiment"""
+    torch_deterministic: bool = True
+    """if toggled, `torch.backends.cudnn.deterministic=False`"""
+    cuda: bool = True
+    """if toggled, cuda will be enabled by default"""
+    track: bool = False
+    """if toggled, this experiment will be tracked with Weights and Biases"""
+    wandb_project_name: str = "cleanRL"
+    """the wandb's project name"""
+    wandb_entity: str = None
+    """the entity (team) of wandb's project"""
+    capture_video: bool = False
+    """whether to capture videos of the agent performances (check out `videos` folder)"""
+
+    # Algorithm specific arguments
+    env_id: str = "Breakout-v5"
+    """the id of the environment"""
+    total_timesteps: int = 10000000
+    """total timesteps of the experiments"""
+    learning_rate: float = 2.5e-4
+    """the learning rate of the optimizer"""
+    num_envs: int = 128
+    """the number of parallel game environments"""
+    num_steps: int = 32
+    """the number of steps to run in each environment per policy rollout"""
+    anneal_lr: bool = True
+    """Toggle learning rate annealing for policy and value networks"""
+    gamma: float = 0.99
+    """the discount factor gamma"""
+    num_minibatches: int = 32
+    """the number of mini-batches"""
+    update_epochs: int = 2
+    """the K epochs to update the policy"""
+    max_grad_norm: float = 10.0
+    """the maximum norm for the gradient clipping"""
+    start_e: float = 1
+    """the starting epsilon for exploration"""
+    end_e: float = 0.01
+    """the ending epsilon for exploration"""
+    exploration_fraction: float = 0.10
+    """the fraction of `total_timesteps` it takes from start_e to end_e"""
+    q_lambda: float = 0.65
+    """the lambda for the Q-Learning algorithm"""
+
+    # to be filled in runtime
+    batch_size: int = 0
+    """the batch size (computed in runtime)"""
+    minibatch_size: int = 0
+    """the mini-batch size (computed in runtime)"""
+    num_iterations: int = 0
+    """the number of iterations (computed in runtime)"""
+
+
+class RecordEpisodeStatistics(gym.Wrapper):
+    def __init__(self, env, deque_size=100):
+        super().__init__(env)
+        self.num_envs = getattr(env, "num_envs", 1)
+        self.episode_returns = None
+        self.episode_lengths = None
+
+    def reset(self, **kwargs):
+        observations = super().reset(**kwargs)
+        self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)
+        self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
+        self.lives = np.zeros(self.num_envs, dtype=np.int32)
+        self.returned_episode_returns = np.zeros(self.num_envs, dtype=np.float32)
+        self.returned_episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
+        return observations
+
+    def step(self, action):
+        observations, rewards, dones, infos = super().step(action)
+        self.episode_returns += infos["reward"]
+        self.episode_lengths += 1
+        self.returned_episode_returns[:] = self.episode_returns
+        self.returned_episode_lengths[:] = self.episode_lengths
+        self.episode_returns *= 1 - infos["terminated"]
+        self.episode_lengths *= 1 - infos["terminated"]
+        infos["r"] = self.returned_episode_returns
+        infos["l"] = self.returned_episode_lengths
+        return (
+            observations,
+            rewards,
+            dones,
+            infos,
+        )
+
+# ALGO LOGIC: initialize agent here:
+class QNetwork(nn.Module):
+    def __init__(self, env):
+        super().__init__()
+        self.network = nn.Sequential(
+            nn.Conv2d(4, 32, 8, stride=4),
+            nn.LayerNorm([32, 20, 20]),
+            nn.ReLU(),
+            nn.Conv2d(32, 64, 4, stride=2),
+            nn.LayerNorm([64, 9, 9]),
+            nn.ReLU(),
+            nn.Conv2d(64, 64, 3, stride=1),
+            nn.LayerNorm([64, 7, 7]),
+            nn.ReLU(),
+            nn.Flatten(),
+            nn.Linear(3136, 512),
+            nn.LayerNorm(512),
+            nn.ReLU(),
+            nn.Linear(512, env.single_action_space.n),
+        )
+
+    def forward(self, x):
+        return self.network(x / 255.0)
+    
+def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
+    slope = (end_e - start_e) / duration
+    return max(slope * t + start_e, end_e)
+
+if __name__ == "__main__":
+    args = tyro.cli(Args)
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    args.num_iterations = args.total_timesteps // args.batch_size
+    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer = SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
+    )
+
+    # TRY NOT TO MODIFY: seeding
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    # env setup
+    envs = envpool.make(
+        args.env_id,
+        env_type="gym",
+        num_envs=args.num_envs,
+        episodic_life=True,
+        reward_clip=True,
+        seed=args.seed,
+    )
+    envs.num_envs = args.num_envs
+    envs.single_action_space = envs.action_space
+    envs.single_observation_space = envs.observation_space
+    envs = RecordEpisodeStatistics(envs)
+    assert isinstance(envs.action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    q_network = QNetwork(envs).to(device)
+    optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate)
+
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    avg_returns = deque(maxlen=20)
+
+    # TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time = time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+
+    for iteration in range(1, args.num_iterations + 1):
+        # Annealing the rate if instructed to do so.
+        if args.anneal_lr:
+            frac = 1.0 - (iteration - 1.0) / args.num_iterations
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"] = lrnow
+
+        for step in range(0, args.num_steps):
+            global_step += args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
+            if random.random() < epsilon:
+                action = torch.randint(0, envs.single_action_space.n, (envs.num_envs,))
+            else:
+                with torch.no_grad():
+                    q_values = q_network(next_obs)
+                    action = torch.argmax(q_values, dim=1)
+            actions[step] = action
+            
+            # TRY NOT TO MODIFY: execute the game and log data.
+            next_obs, reward, next_done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)
+
+            for idx, d in enumerate(next_done):
+                if d and info["lives"][idx] == 0:
+                    print(f"global_step={global_step}, episodic_return={info['r'][idx]}")
+                    avg_returns.append(info["r"][idx])
+                    writer.add_scalar("charts/avg_episodic_return", np.average(avg_returns), global_step)
+                    writer.add_scalar("charts/episodic_return", info["r"][idx], global_step)
+                    writer.add_scalar("charts/episodic_length", info["l"][idx], global_step)
+
+        # Compute Q(lambda) targets
+        with torch.no_grad():
+            returns = torch.zeros_like(rewards).to(device)
+            for t in reversed(range(args.num_steps)):
+                if t == args.num_steps - 1:
+                    next_value, _ = torch.max(q_network(next_obs), dim=-1)
+                    nextnonterminal = 1.0 - next_done
+                    returns[t] = rewards[t] + args.gamma * next_value * nextnonterminal
+                else:
+                    nextnonterminal = 1.0 - dones[t + 1]
+                    next_value, _ = torch.max(q_network(obs[t + 1]), dim=-1)
+                    returns[t] = rewards[t] + args.gamma * (args.q_lambda * returns[t+1] + (1-args.q_lambda) * next_value * nextnonterminal)
+
+        # flatten the batch
+        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+        b_returns = returns.reshape(-1)
+
+        # Optimizing the Q-network
+        b_inds = np.arange(args.batch_size)
+        for epoch in range(args.update_epochs):
+            np.random.shuffle(b_inds)
+            for start in range(0, args.batch_size, args.minibatch_size):
+                end = start + args.minibatch_size
+                mb_inds = b_inds[start:end]
+
+                old_val = q_network(b_obs[mb_inds]).gather(1, b_actions[mb_inds].unsqueeze(-1).long()).squeeze()
+                loss = F.mse_loss(b_returns[mb_inds], old_val)
+
+                # optimize the model
+                optimizer.zero_grad()
+                loss.backward()
+                nn.utils.clip_grad_norm_(q_network.parameters(), args.max_grad_norm)
+                optimizer.step()
+
+        writer.add_scalar("losses/td_loss", loss, global_step)
+        writer.add_scalar("losses/q_values", old_val.mean().item(), global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
+
+    envs.close()
+    writer.close()

From 21226d78941b583fc7941a10c469c4f78769d011 Mon Sep 17 00:00:00 2001
From: roger-creus <creus99@gmail.com>
Date: Wed, 17 Jul 2024 14:37:19 -0400
Subject: [PATCH 3/8] Run pre-commit run --all-files

---
 cleanrl/pqn.py               | 25 +++++++++++++++----------
 cleanrl/pqn_atari_envpool.py | 14 ++++++++++----
 2 files changed, 25 insertions(+), 14 deletions(-)

diff --git a/cleanrl/pqn.py b/cleanrl/pqn.py
index 1b9b3b7bc..f29060e01 100644
--- a/cleanrl/pqn.py
+++ b/cleanrl/pqn.py
@@ -2,6 +2,7 @@
 import os
 import random
 import time
+from collections import deque
 from dataclasses import dataclass
 
 import envpool
@@ -12,9 +13,8 @@
 import torch.nn.functional as F
 import torch.optim as optim
 import tyro
-from stable_baselines3.common.buffers import ReplayBuffer
 from torch.utils.tensorboard import SummaryWriter
-from collections import deque
+
 
 @dataclass
 class Args:
@@ -34,7 +34,7 @@ class Args:
     """the entity (team) of wandb's project"""
     capture_video: bool = False
     """whether to capture videos of the agent performances (check out `videos` folder)"""
-    
+
     # Algorithm specific arguments
     env_id: str = "CartPole-v1"
     """the id of the environment"""
@@ -66,7 +66,8 @@ class Args:
     """the reward scaling factor"""
     q_lambda: float = 0.65
     """the lambda for Q(lambda)"""
-    
+
+
 class RecordEpisodeStatistics(gym.Wrapper):
     def __init__(self, env, deque_size=100):
         super().__init__(env)
@@ -100,11 +101,12 @@ def step(self, action):
             infos,
         )
 
+
 # ALGO LOGIC: initialize agent here:
 class QNetwork(nn.Module):
     def __init__(self, env):
         super().__init__()
-        
+
         self.network = nn.Sequential(
             nn.Linear(np.array(env.single_observation_space.shape).prod(), 120),
             nn.LayerNorm(120),
@@ -123,6 +125,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     slope = (end_e - start_e) / duration
     return max(slope * t + start_e, end_e)
 
+
 if __name__ == "__main__":
     args = tyro.cli(Args)
     args.batch_size = int(args.num_envs * args.num_steps)
@@ -178,14 +181,14 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
     dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
     avg_returns = deque(maxlen=20)
-    
+
     global_step = 0
     start_time = time.time()
 
     # TRY NOT TO MODIFY: start the game
     next_obs = torch.Tensor(envs.reset()).to(device)
     next_done = torch.zeros(args.num_envs).to(device)
-    
+
     for iteration in range(1, args.num_iterations + 1):
         # Annealing the rate if instructed to do so.
         if args.anneal_lr:
@@ -196,7 +199,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
         for step in range(0, args.num_steps):
             global_step += args.num_envs
             obs[step] = next_obs
-            dones[step] = next_done           
+            dones[step] = next_done
 
             epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
             if random.random() < epsilon:
@@ -211,7 +214,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
             next_obs, reward, next_done, info = envs.step(action.cpu().numpy())
             rewards[step] = torch.tensor(reward).to(device).view(-1) * args.rew_scale
             next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)
-            
+
             # TRY NOT TO MODIFY: record rewards for plotting purposes
             for idx, d in enumerate(next_done):
                 if d:
@@ -232,7 +235,9 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
                 else:
                     nextnonterminal = 1.0 - dones[t + 1]
                     next_value, _ = torch.max(q_network(obs[t + 1]), dim=-1)
-                    returns[t] = rewards[t] + args.gamma * (args.q_lambda * returns[t+1] + (1-args.q_lambda) * next_value * nextnonterminal)
+                    returns[t] = rewards[t] + args.gamma * (
+                        args.q_lambda * returns[t + 1] + (1 - args.q_lambda) * next_value * nextnonterminal
+                    )
 
         # flatten the batch
         b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
diff --git a/cleanrl/pqn_atari_envpool.py b/cleanrl/pqn_atari_envpool.py
index e8ddc8e8e..79b2541d6 100644
--- a/cleanrl/pqn_atari_envpool.py
+++ b/cleanrl/pqn_atari_envpool.py
@@ -10,10 +10,11 @@
 import numpy as np
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 import torch.optim as optim
 import tyro
 from torch.utils.tensorboard import SummaryWriter
-import torch.nn.functional as F
+
 
 @dataclass
 class Args:
@@ -106,6 +107,7 @@ def step(self, action):
             infos,
         )
 
+
 # ALGO LOGIC: initialize agent here:
 class QNetwork(nn.Module):
     def __init__(self, env):
@@ -129,11 +131,13 @@ def __init__(self, env):
 
     def forward(self, x):
         return self.network(x / 255.0)
-    
+
+
 def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     slope = (end_e - start_e) / duration
     return max(slope * t + start_e, end_e)
 
+
 if __name__ == "__main__":
     args = tyro.cli(Args)
     args.batch_size = int(args.num_envs * args.num_steps)
@@ -217,7 +221,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
                     q_values = q_network(next_obs)
                     action = torch.argmax(q_values, dim=1)
             actions[step] = action
-            
+
             # TRY NOT TO MODIFY: execute the game and log data.
             next_obs, reward, next_done, info = envs.step(action.cpu().numpy())
             rewards[step] = torch.tensor(reward).to(device).view(-1)
@@ -242,7 +246,9 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
                 else:
                     nextnonterminal = 1.0 - dones[t + 1]
                     next_value, _ = torch.max(q_network(obs[t + 1]), dim=-1)
-                    returns[t] = rewards[t] + args.gamma * (args.q_lambda * returns[t+1] + (1-args.q_lambda) * next_value * nextnonterminal)
+                    returns[t] = rewards[t] + args.gamma * (
+                        args.q_lambda * returns[t + 1] + (1 - args.q_lambda) * next_value * nextnonterminal
+                    )
 
         # flatten the batch
         b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)

From fba899485fd0c5d6eccffd922f3d925e6d9d58f1 Mon Sep 17 00:00:00 2001
From: roger-creus <creus99@gmail.com>
Date: Wed, 17 Jul 2024 23:52:40 -0400
Subject: [PATCH 4/8] each env has its own probability of random.random() <
 epsilon -- i.e. some envs can explore and some exploit, like in the official
 implementation

---
 cleanrl/pqn.py               | 14 ++++++++------
 cleanrl/pqn_atari_envpool.py | 14 ++++++++------
 2 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/cleanrl/pqn.py b/cleanrl/pqn.py
index f29060e01..a4947afd5 100644
--- a/cleanrl/pqn.py
+++ b/cleanrl/pqn.py
@@ -202,12 +202,14 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
             dones[step] = next_done
 
             epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
-            if random.random() < epsilon:
-                action = torch.randint(0, envs.single_action_space.n, (envs.num_envs,))
-            else:
-                with torch.no_grad():
-                    q_values = q_network(next_obs)
-                    action = torch.argmax(q_values, dim=1)
+
+            random_actions = torch.randint(0, envs.single_action_space.n, (args.num_envs,)).to(device)
+            with torch.no_grad():
+                q_values = q_network(next_obs)
+                max_actions = torch.argmax(q_values, dim=1)
+
+            explore = (torch.rand((args.num_envs,)).to(device) < epsilon)
+            action = torch.where(explore, random_actions, max_actions)
             actions[step] = action
 
             # TRY NOT TO MODIFY: execute the game and log data.
diff --git a/cleanrl/pqn_atari_envpool.py b/cleanrl/pqn_atari_envpool.py
index 79b2541d6..06277d730 100644
--- a/cleanrl/pqn_atari_envpool.py
+++ b/cleanrl/pqn_atari_envpool.py
@@ -214,12 +214,14 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
             dones[step] = next_done
 
             epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
-            if random.random() < epsilon:
-                action = torch.randint(0, envs.single_action_space.n, (envs.num_envs,))
-            else:
-                with torch.no_grad():
-                    q_values = q_network(next_obs)
-                    action = torch.argmax(q_values, dim=1)
+
+            random_actions = torch.randint(0, envs.single_action_space.n, (args.num_envs,)).to(device)
+            with torch.no_grad():
+                q_values = q_network(next_obs)
+                max_actions = torch.argmax(q_values, dim=1)
+
+            explore = (torch.rand((args.num_envs,)).to(device) < epsilon)
+            action = torch.where(explore, random_actions, max_actions)
             actions[step] = action
 
             # TRY NOT TO MODIFY: execute the game and log data.

From b6a12c1255d4f3bc86a59f59ee40e8195060317c Mon Sep 17 00:00:00 2001
From: roger-creus <creus99@gmail.com>
Date: Thu, 25 Jul 2024 18:28:19 -0400
Subject: [PATCH 5/8] added pqn with lstm. works on breakout :)

---
 cleanrl/pqn_atari_lstm_envpool.py | 329 ++++++++++++++++++++++++++++++
 1 file changed, 329 insertions(+)
 create mode 100644 cleanrl/pqn_atari_lstm_envpool.py

diff --git a/cleanrl/pqn_atari_lstm_envpool.py b/cleanrl/pqn_atari_lstm_envpool.py
new file mode 100644
index 000000000..d7d3be28e
--- /dev/null
+++ b/cleanrl/pqn_atari_lstm_envpool.py
@@ -0,0 +1,329 @@
+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_envpoolpy
+import os
+import random
+import time
+from collections import deque
+from dataclasses import dataclass
+
+import envpool
+import gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import tyro
+from torch.utils.tensorboard import SummaryWriter
+
+
+@dataclass
+class Args:
+    exp_name: str = os.path.basename(__file__)[: -len(".py")]
+    """the name of this experiment"""
+    seed: int = 1
+    """seed of the experiment"""
+    torch_deterministic: bool = True
+    """if toggled, `torch.backends.cudnn.deterministic=False`"""
+    cuda: bool = True
+    """if toggled, cuda will be enabled by default"""
+    track: bool = False
+    """if toggled, this experiment will be tracked with Weights and Biases"""
+    wandb_project_name: str = "cleanRL"
+    """the wandb's project name"""
+    wandb_entity: str = None
+    """the entity (team) of wandb's project"""
+    capture_video: bool = False
+    """whether to capture videos of the agent performances (check out `videos` folder)"""
+
+    # Algorithm specific arguments
+    env_id: str = "Breakout-v5"
+    """the id of the environment"""
+    total_timesteps: int = 10000000
+    """total timesteps of the experiments"""
+    learning_rate: float = 2.5e-4
+    """the learning rate of the optimizer"""
+    num_envs: int = 8
+    """the number of parallel game environments"""
+    num_steps: int = 128
+    """the number of steps to run in each environment per policy rollout"""
+    anneal_lr: bool = True
+    """Toggle learning rate annealing for policy and value networks"""
+    gamma: float = 0.99
+    """the discount factor gamma"""
+    num_minibatches: int = 4
+    """the number of mini-batches"""
+    update_epochs: int = 2
+    """the K epochs to update the policy"""
+    max_grad_norm: float = 0.5
+    """the maximum norm for the gradient clipping"""
+    start_e: float = 1
+    """the starting epsilon for exploration"""
+    end_e: float = 0.01
+    """the ending epsilon for exploration"""
+    exploration_fraction: float = 0.10
+    """the fraction of `total_timesteps` it takes from start_e to end_e"""
+    q_lambda: float = 0.65
+    """the lambda for the Q-Learning algorithm"""
+
+    # to be filled in runtime
+    batch_size: int = 0
+    """the batch size (computed in runtime)"""
+    minibatch_size: int = 0
+    """the mini-batch size (computed in runtime)"""
+    num_iterations: int = 0
+    """the number of iterations (computed in runtime)"""
+
+
+class RecordEpisodeStatistics(gym.Wrapper):
+    def __init__(self, env, deque_size=100):
+        super().__init__(env)
+        self.num_envs = getattr(env, "num_envs", 1)
+        self.episode_returns = None
+        self.episode_lengths = None
+
+    def reset(self, **kwargs):
+        observations = super().reset(**kwargs)
+        self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)
+        self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
+        self.lives = np.zeros(self.num_envs, dtype=np.int32)
+        self.returned_episode_returns = np.zeros(self.num_envs, dtype=np.float32)
+        self.returned_episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
+        return observations
+
+    def step(self, action):
+        observations, rewards, dones, infos = super().step(action)
+        self.episode_returns += infos["reward"]
+        self.episode_lengths += 1
+        self.returned_episode_returns[:] = self.episode_returns
+        self.returned_episode_lengths[:] = self.episode_lengths
+        self.episode_returns *= 1 - infos["terminated"]
+        self.episode_lengths *= 1 - infos["terminated"]
+        infos["r"] = self.returned_episode_returns
+        infos["l"] = self.returned_episode_lengths
+        return (
+            observations,
+            rewards,
+            dones,
+            infos,
+        )
+
+
+# ALGO LOGIC: initialize agent here:
+class QNetwork(nn.Module):
+    def __init__(self, env):
+        super().__init__()
+        self.network = nn.Sequential(
+            nn.Conv2d(1, 32, 8, stride=4),
+            nn.LayerNorm([32, 20, 20]),
+            nn.ReLU(),
+            nn.Conv2d(32, 64, 4, stride=2),
+            nn.LayerNorm([64, 9, 9]),
+            nn.ReLU(),
+            nn.Conv2d(64, 64, 3, stride=1),
+            nn.LayerNorm([64, 7, 7]),
+            nn.ReLU(),
+            nn.Flatten(),
+            nn.Linear(3136, 512),
+            nn.LayerNorm(512),
+            nn.ReLU(),
+        )
+        self.lstm = nn.LSTM(512, 128)
+        self.head = nn.Linear(128, env.single_action_space.n)
+
+    
+    def get_states(self, x, lstm_state, done):
+        hidden = self.network(x / 255.0)
+
+        # LSTM logic
+        batch_size = lstm_state[0].shape[1]
+        hidden = hidden.reshape((-1, batch_size, self.lstm.input_size))
+        done = done.reshape((-1, batch_size))
+        new_hidden = []
+        for h, d in zip(hidden, done):
+            h, lstm_state = self.lstm(
+                h.unsqueeze(0),
+                (
+                    (1.0 - d).view(1, -1, 1) * lstm_state[0],
+                    (1.0 - d).view(1, -1, 1) * lstm_state[1],
+                ),
+            )
+            new_hidden += [h]
+        new_hidden = torch.flatten(torch.cat(new_hidden), 0, 1)
+        return new_hidden, lstm_state
+
+    def forward(self, x, lstm_state, done):
+        hidden, lstm_state = self.get_states(x, lstm_state, done)
+        return self.head(hidden), lstm_state
+
+
+def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
+    slope = (end_e - start_e) / duration
+    return max(slope * t + start_e, end_e)
+
+if __name__ == "__main__":
+    args = tyro.cli(Args)
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    args.num_iterations = args.total_timesteps // args.batch_size
+    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer = SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
+    )
+
+    # TRY NOT TO MODIFY: seeding
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    # env setup
+    envs = envpool.make(
+        args.env_id,
+        env_type="gym",
+        num_envs=args.num_envs,
+        episodic_life=True,
+        reward_clip=True,
+        seed=args.seed,
+        stack_num=1,
+    )
+    envs.num_envs = args.num_envs
+    envs.single_action_space = envs.action_space
+    envs.single_observation_space = envs.observation_space
+    envs = RecordEpisodeStatistics(envs)
+    assert isinstance(envs.action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    q_network = QNetwork(envs).to(device)
+    optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate)
+
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    avg_returns = deque(maxlen=20)
+
+
+    # TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time = time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    
+    next_lstm_state = (
+        torch.zeros(q_network.lstm.num_layers, args.num_envs, q_network.lstm.hidden_size).to(device),
+        torch.zeros(q_network.lstm.num_layers, args.num_envs, q_network.lstm.hidden_size).to(device),
+    )  # hidden and cell states (see https://youtu.be/8HyCNIVRbSU)
+
+    for iteration in range(1, args.num_iterations + 1):
+        initial_lstm_state = (next_lstm_state[0].clone(), next_lstm_state[1].clone())
+
+        # Annealing the rate if instructed to do so.
+        if args.anneal_lr:
+            frac = 1.0 - (iteration - 1.0) / args.num_iterations
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"] = lrnow
+
+        for step in range(0, args.num_steps):
+            global_step += args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
+
+            random_actions = torch.randint(0, envs.single_action_space.n, (args.num_envs,)).to(device)
+            with torch.no_grad():
+                q_values, next_lstm_state = q_network(next_obs, next_lstm_state, next_done)
+                max_actions = torch.argmax(q_values, dim=1)
+                values[step] = q_values[torch.arange(args.num_envs), max_actions].flatten()
+
+            explore = (torch.rand((args.num_envs,)).to(device) < epsilon)
+            action = torch.where(explore, random_actions, max_actions)
+            actions[step] = action
+
+            # TRY NOT TO MODIFY: execute the game and log data.
+            next_obs, reward, next_done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)
+
+            for idx, d in enumerate(next_done):
+                if d and info["lives"][idx] == 0:
+                    print(f"global_step={global_step}, episodic_return={info['r'][idx]}")
+                    avg_returns.append(info["r"][idx])
+                    writer.add_scalar("charts/avg_episodic_return", np.average(avg_returns), global_step)
+                    writer.add_scalar("charts/episodic_return", info["r"][idx], global_step)
+                    writer.add_scalar("charts/episodic_length", info["l"][idx], global_step)
+
+        # Compute Q(lambda) targets
+        with torch.no_grad():
+            returns = torch.zeros_like(rewards).to(device)
+            for t in reversed(range(args.num_steps)):
+                if t == args.num_steps - 1:
+                    next_value, _ = torch.max(q_network(next_obs, next_lstm_state, next_done)[0], dim=-1)
+                    nextnonterminal = 1.0 - next_done
+                    returns[t] = rewards[t] + args.gamma * next_value * nextnonterminal
+                else:
+                    nextnonterminal = 1.0 - dones[t + 1]
+                    next_value = values[t + 1]
+                    returns[t] = rewards[t] + args.gamma * (
+                        args.q_lambda * returns[t + 1] + (1 - args.q_lambda) * next_value * nextnonterminal
+                    )
+
+        # flatten the batch
+        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+        b_returns = returns.reshape(-1)
+        b_dones = dones.reshape(-1)
+
+        assert args.num_envs % args.num_minibatches == 0
+        envsperbatch = args.num_envs // args.num_minibatches
+        envinds = np.arange(args.num_envs)
+        flatinds = np.arange(args.batch_size).reshape(args.num_steps, args.num_envs)
+
+        # Optimizing the Q-network
+        b_inds = np.arange(args.batch_size)
+        for epoch in range(args.update_epochs):
+            np.random.shuffle(envinds)
+            for start in range(0, args.num_envs, envsperbatch):
+                end = start + envsperbatch
+                mbenvinds = envinds[start:end]
+                mb_inds = flatinds[:, mbenvinds].ravel()  # be really careful about the index
+
+                old_val, _ = q_network(
+                    b_obs[mb_inds],
+                    (initial_lstm_state[0][:, mbenvinds], initial_lstm_state[1][:, mbenvinds]),
+                    b_dones[mb_inds]
+                )
+                old_val = old_val.gather(1, b_actions[mb_inds].unsqueeze(-1).long()).squeeze()
+
+                loss = F.mse_loss(b_returns[mb_inds], old_val)
+
+                # optimize the model
+                optimizer.zero_grad()
+                loss.backward()
+                nn.utils.clip_grad_norm_(q_network.parameters(), args.max_grad_norm)
+                optimizer.step()
+
+        writer.add_scalar("losses/td_loss", loss, global_step)
+        writer.add_scalar("losses/q_values", old_val.mean().item(), global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
+
+    envs.close()
+    writer.close()

From bfe1205136458601438f94e74214625340c7cd99 Mon Sep 17 00:00:00 2001
From: roger-creus <creus99@gmail.com>
Date: Fri, 26 Jul 2024 09:23:43 -0400
Subject: [PATCH 6/8] Use buffer for values instead of re-computing in
 Q(lambda). Speed up 150%

---
 cleanrl/pqn.py               | 6 ++++--
 cleanrl/pqn_atari_envpool.py | 5 +++--
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/cleanrl/pqn.py b/cleanrl/pqn.py
index a4947afd5..30e346f2a 100644
--- a/cleanrl/pqn.py
+++ b/cleanrl/pqn.py
@@ -180,6 +180,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
     rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
     dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
     avg_returns = deque(maxlen=20)
 
     global_step = 0
@@ -207,6 +208,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
             with torch.no_grad():
                 q_values = q_network(next_obs)
                 max_actions = torch.argmax(q_values, dim=1)
+                values[step] = q_values[torch.arange(args.num_envs), max_actions].flatten()
 
             explore = (torch.rand((args.num_envs,)).to(device) < epsilon)
             action = torch.where(explore, random_actions, max_actions)
@@ -236,11 +238,11 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
                     returns[t] = rewards[t] + args.gamma * next_value * nextnonterminal
                 else:
                     nextnonterminal = 1.0 - dones[t + 1]
-                    next_value, _ = torch.max(q_network(obs[t + 1]), dim=-1)
+                    next_value = values[t + 1]
                     returns[t] = rewards[t] + args.gamma * (
                         args.q_lambda * returns[t + 1] + (1 - args.q_lambda) * next_value * nextnonterminal
                     )
-
+                    
         # flatten the batch
         b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
         b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
diff --git a/cleanrl/pqn_atari_envpool.py b/cleanrl/pqn_atari_envpool.py
index 06277d730..55166e507 100644
--- a/cleanrl/pqn_atari_envpool.py
+++ b/cleanrl/pqn_atari_envpool.py
@@ -107,7 +107,6 @@ def step(self, action):
             infos,
         )
 
-
 # ALGO LOGIC: initialize agent here:
 class QNetwork(nn.Module):
     def __init__(self, env):
@@ -193,6 +192,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
     rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
     dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
     avg_returns = deque(maxlen=20)
 
     # TRY NOT TO MODIFY: start the game
@@ -219,6 +219,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
             with torch.no_grad():
                 q_values = q_network(next_obs)
                 max_actions = torch.argmax(q_values, dim=1)
+                values[step] = q_values[torch.arange(args.num_envs), max_actions].flatten()
 
             explore = (torch.rand((args.num_envs,)).to(device) < epsilon)
             action = torch.where(explore, random_actions, max_actions)
@@ -247,7 +248,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
                     returns[t] = rewards[t] + args.gamma * next_value * nextnonterminal
                 else:
                     nextnonterminal = 1.0 - dones[t + 1]
-                    next_value, _ = torch.max(q_network(obs[t + 1]), dim=-1)
+                    next_value = values[t + 1]
                     returns[t] = rewards[t] + args.gamma * (
                         args.q_lambda * returns[t + 1] + (1 - args.q_lambda) * next_value * nextnonterminal
                     )

From 10334fa9125c3ba8ba8f9feca462b3ef83f80060 Mon Sep 17 00:00:00 2001
From: roger-creus <creus99@gmail.com>
Date: Fri, 25 Oct 2024 11:32:49 -0400
Subject: [PATCH 7/8] default rollout hyperparameters in PQN are now equal to
 PPO. same for PQN Lstm. Ran validation experiments. Reverting undesired
 changes in DQN. WIP documentation

---
 cleanrl/dqn.py                                |   4 +-
 cleanrl/pqn.py                                | 117 ++++-----
 cleanrl/pqn_atari_envpool.py                  |  30 ++-
 ...m_envpool.py => pqn_atari_envpool_lstm.py} |  31 ++-
 docs/rl-algorithms/pqn.md                     | 236 ++++++++++++++++++
 5 files changed, 319 insertions(+), 99 deletions(-)
 rename cleanrl/{pqn_atari_lstm_envpool.py => pqn_atari_envpool_lstm.py} (93%)
 create mode 100644 docs/rl-algorithms/pqn.md

diff --git a/cleanrl/dqn.py b/cleanrl/dqn.py
index 7ca633a44..e74e289c3 100644
--- a/cleanrl/dqn.py
+++ b/cleanrl/dqn.py
@@ -69,8 +69,6 @@ class Args:
     """timestep to start learning"""
     train_frequency: int = 10
     """the frequency of training"""
-    rew_scale: float = 0.1
-    """the reward scaling factor"""
 
 
 def make_env(env_id, seed, idx, capture_video, run_name):
@@ -195,7 +193,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
         for idx, trunc in enumerate(truncations):
             if trunc:
                 real_next_obs[idx] = infos["final_observation"][idx]
-        rb.add(obs, real_next_obs, actions, rewards * args.rew_scale, terminations, infos)
+        rb.add(obs, real_next_obs, actions, rewards, terminations, infos)
 
         # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
         obs = next_obs
diff --git a/cleanrl/pqn.py b/cleanrl/pqn.py
index 30e346f2a..2f9874803 100644
--- a/cleanrl/pqn.py
+++ b/cleanrl/pqn.py
@@ -2,11 +2,9 @@
 import os
 import random
 import time
-from collections import deque
 from dataclasses import dataclass
 
-import envpool
-import gym
+import gymnasium as gym
 import numpy as np
 import torch
 import torch.nn as nn
@@ -42,13 +40,13 @@ class Args:
     """total timesteps of the experiments"""
     learning_rate: float = 2.5e-4
     """the learning rate of the optimizer"""
-    num_envs: int = 32
+    num_envs: int = 4
     """the number of parallel game environments"""
-    num_steps: int = 64
+    num_steps: int = 128
     """the number of steps to run for each environment per update"""
-    num_minibatches: int = 16
+    num_minibatches: int = 4
     """the number of mini-batches"""
-    update_epochs: int = 2
+    update_epochs: int = 4
     """the K epochs to update the policy"""
     anneal_lr: bool = True
     """Toggle learning rate annealing"""
@@ -62,44 +60,29 @@ class Args:
     """the fraction of `total_timesteps` it takes from start_e to end_e"""
     max_grad_norm: float = 10.0
     """the maximum norm for the gradient clipping"""
-    rew_scale: float = 0.1
-    """the reward scaling factor"""
     q_lambda: float = 0.65
     """the lambda for Q(lambda)"""
 
 
-class RecordEpisodeStatistics(gym.Wrapper):
-    def __init__(self, env, deque_size=100):
-        super().__init__(env)
-        self.num_envs = getattr(env, "num_envs", 1)
-        self.episode_returns = None
-        self.episode_lengths = None
-
-    def reset(self, **kwargs):
-        observations = super().reset(**kwargs)
-        self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)
-        self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
-        self.lives = np.zeros(self.num_envs, dtype=np.int32)
-        self.returned_episode_returns = np.zeros(self.num_envs, dtype=np.float32)
-        self.returned_episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
-        return observations
-
-    def step(self, action):
-        observations, rewards, dones, infos = super().step(action)
-        self.episode_returns += rewards
-        self.episode_lengths += 1
-        self.returned_episode_returns[:] = self.episode_returns
-        self.returned_episode_lengths[:] = self.episode_lengths
-        self.episode_returns *= 1 - dones
-        self.episode_lengths *= 1 - dones
-        infos["r"] = self.returned_episode_returns
-        infos["l"] = self.returned_episode_lengths
-        return (
-            observations,
-            rewards,
-            dones,
-            infos,
-        )
+def make_env(env_id, seed, idx, capture_video, run_name):
+    def thunk():
+        if capture_video and idx == 0:
+            env = gym.make(env_id, render_mode="rgb_array")
+            env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
+        else:
+            env = gym.make(env_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        env.action_space.seed(seed)
+
+        return env
+
+    return thunk
+
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
 
 
 # ALGO LOGIC: initialize agent here:
@@ -108,13 +91,13 @@ def __init__(self, env):
         super().__init__()
 
         self.network = nn.Sequential(
-            nn.Linear(np.array(env.single_observation_space.shape).prod(), 120),
+            layer_init(nn.Linear(np.array(env.single_observation_space.shape).prod(), 120)),
             nn.LayerNorm(120),
             nn.ReLU(),
-            nn.Linear(120, 84),
+            layer_init(nn.Linear(120, 84)),
             nn.LayerNorm(84),
             nn.ReLU(),
-            nn.Linear(84, env.single_action_space.n),
+            layer_init(nn.Linear(84, env.single_action_space.n)),
         )
 
     def forward(self, x):
@@ -159,21 +142,14 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
 
     # env setup
-    envs = envpool.make(
-        args.env_id,
-        env_type="gym",
-        num_envs=args.num_envs,
-        seed=args.seed,
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
     )
-    envs.num_envs = args.num_envs
-    envs.single_action_space = envs.action_space
-    envs.single_observation_space = envs.observation_space
-    envs = RecordEpisodeStatistics(envs)
-    assert isinstance(envs.action_space, gym.spaces.Discrete), "only discrete action space is supported"
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
 
     # agent setup
     q_network = QNetwork(envs).to(device)
-    optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate)
+    optimizer = optim.RAdam(q_network.parameters(), lr=args.learning_rate)
 
     # storage setup
     obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
@@ -181,13 +157,12 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
     dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
     values = torch.zeros((args.num_steps, args.num_envs)).to(device)
-    avg_returns = deque(maxlen=20)
 
+    # TRY NOT TO MODIFY: start the game
     global_step = 0
     start_time = time.time()
-
-    # TRY NOT TO MODIFY: start the game
-    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_obs, _ = envs.reset(seed=args.seed)
+    next_obs = torch.Tensor(next_obs).to(device)
     next_done = torch.zeros(args.num_envs).to(device)
 
     for iteration in range(1, args.num_iterations + 1):
@@ -203,30 +178,28 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
             dones[step] = next_done
 
             epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
-
             random_actions = torch.randint(0, envs.single_action_space.n, (args.num_envs,)).to(device)
             with torch.no_grad():
                 q_values = q_network(next_obs)
                 max_actions = torch.argmax(q_values, dim=1)
                 values[step] = q_values[torch.arange(args.num_envs), max_actions].flatten()
 
-            explore = (torch.rand((args.num_envs,)).to(device) < epsilon)
+            explore = torch.rand((args.num_envs,)).to(device) < epsilon
             action = torch.where(explore, random_actions, max_actions)
             actions[step] = action
 
             # TRY NOT TO MODIFY: execute the game and log data.
-            next_obs, reward, next_done, info = envs.step(action.cpu().numpy())
-            rewards[step] = torch.tensor(reward).to(device).view(-1) * args.rew_scale
+            next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy())
+            next_done = np.logical_or(terminations, truncations)
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
             next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)
 
-            # TRY NOT TO MODIFY: record rewards for plotting purposes
-            for idx, d in enumerate(next_done):
-                if d:
-                    print(f"global_step={global_step}, episodic_return={info['r'][idx]}")
-                    avg_returns.append(info["r"][idx])
-                    writer.add_scalar("charts/avg_episodic_return", np.average(avg_returns), global_step)
-                    writer.add_scalar("charts/episodic_return", info["r"][idx], global_step)
-                    writer.add_scalar("charts/episodic_length", info["l"][idx], global_step)
+            if "final_info" in infos:
+                for info in infos["final_info"]:
+                    if info and "episode" in info:
+                        print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
+                        writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
+                        writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
 
         # Compute Q(lambda) targets
         with torch.no_grad():
@@ -242,7 +215,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
                     returns[t] = rewards[t] + args.gamma * (
                         args.q_lambda * returns[t + 1] + (1 - args.q_lambda) * next_value * nextnonterminal
                     )
-                    
+
         # flatten the batch
         b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
         b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
diff --git a/cleanrl/pqn_atari_envpool.py b/cleanrl/pqn_atari_envpool.py
index 55166e507..f1d7deb3d 100644
--- a/cleanrl/pqn_atari_envpool.py
+++ b/cleanrl/pqn_atari_envpool.py
@@ -42,17 +42,17 @@ class Args:
     """total timesteps of the experiments"""
     learning_rate: float = 2.5e-4
     """the learning rate of the optimizer"""
-    num_envs: int = 128
+    num_envs: int = 8
     """the number of parallel game environments"""
-    num_steps: int = 32
+    num_steps: int = 128
     """the number of steps to run in each environment per policy rollout"""
     anneal_lr: bool = True
     """Toggle learning rate annealing for policy and value networks"""
     gamma: float = 0.99
     """the discount factor gamma"""
-    num_minibatches: int = 32
+    num_minibatches: int = 4
     """the number of mini-batches"""
-    update_epochs: int = 2
+    update_epochs: int = 4
     """the K epochs to update the policy"""
     max_grad_norm: float = 10.0
     """the maximum norm for the gradient clipping"""
@@ -107,25 +107,31 @@ def step(self, action):
             infos,
         )
 
-# ALGO LOGIC: initialize agent here:
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+
 class QNetwork(nn.Module):
     def __init__(self, env):
         super().__init__()
         self.network = nn.Sequential(
-            nn.Conv2d(4, 32, 8, stride=4),
+            layer_init(nn.Conv2d(4, 32, 8, stride=4)),
             nn.LayerNorm([32, 20, 20]),
             nn.ReLU(),
-            nn.Conv2d(32, 64, 4, stride=2),
+            layer_init(nn.Conv2d(32, 64, 4, stride=2)),
             nn.LayerNorm([64, 9, 9]),
             nn.ReLU(),
-            nn.Conv2d(64, 64, 3, stride=1),
+            layer_init(nn.Conv2d(64, 64, 3, stride=1)),
             nn.LayerNorm([64, 7, 7]),
             nn.ReLU(),
             nn.Flatten(),
-            nn.Linear(3136, 512),
+            layer_init(nn.Linear(3136, 512)),
             nn.LayerNorm(512),
             nn.ReLU(),
-            nn.Linear(512, env.single_action_space.n),
+            layer_init(nn.Linear(512, env.single_action_space.n)),
         )
 
     def forward(self, x):
@@ -185,7 +191,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     assert isinstance(envs.action_space, gym.spaces.Discrete), "only discrete action space is supported"
 
     q_network = QNetwork(envs).to(device)
-    optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate)
+    optimizer = optim.RAdam(q_network.parameters(), lr=args.learning_rate)
 
     # ALGO Logic: Storage setup
     obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
@@ -221,7 +227,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
                 max_actions = torch.argmax(q_values, dim=1)
                 values[step] = q_values[torch.arange(args.num_envs), max_actions].flatten()
 
-            explore = (torch.rand((args.num_envs,)).to(device) < epsilon)
+            explore = torch.rand((args.num_envs,)).to(device) < epsilon
             action = torch.where(explore, random_actions, max_actions)
             actions[step] = action
 
diff --git a/cleanrl/pqn_atari_lstm_envpool.py b/cleanrl/pqn_atari_envpool_lstm.py
similarity index 93%
rename from cleanrl/pqn_atari_lstm_envpool.py
rename to cleanrl/pqn_atari_envpool_lstm.py
index d7d3be28e..3b21d5cff 100644
--- a/cleanrl/pqn_atari_lstm_envpool.py
+++ b/cleanrl/pqn_atari_envpool_lstm.py
@@ -15,7 +15,6 @@
 import tyro
 from torch.utils.tensorboard import SummaryWriter
 
-
 @dataclass
 class Args:
     exp_name: str = os.path.basename(__file__)[: -len(".py")]
@@ -52,7 +51,7 @@ class Args:
     """the discount factor gamma"""
     num_minibatches: int = 4
     """the number of mini-batches"""
-    update_epochs: int = 2
+    update_epochs: int = 4
     """the K epochs to update the policy"""
     max_grad_norm: float = 0.5
     """the maximum norm for the gradient clipping"""
@@ -108,29 +107,38 @@ def step(self, action):
         )
 
 
-# ALGO LOGIC: initialize agent here:
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+
 class QNetwork(nn.Module):
     def __init__(self, env):
         super().__init__()
         self.network = nn.Sequential(
-            nn.Conv2d(1, 32, 8, stride=4),
+            layer_init(nn.Conv2d(1, 32, 8, stride=4)),
             nn.LayerNorm([32, 20, 20]),
             nn.ReLU(),
-            nn.Conv2d(32, 64, 4, stride=2),
+            layer_init(nn.Conv2d(32, 64, 4, stride=2)),
             nn.LayerNorm([64, 9, 9]),
             nn.ReLU(),
-            nn.Conv2d(64, 64, 3, stride=1),
+            layer_init(nn.Conv2d(64, 64, 3, stride=1)),
             nn.LayerNorm([64, 7, 7]),
             nn.ReLU(),
             nn.Flatten(),
-            nn.Linear(3136, 512),
+            layer_init(nn.Linear(3136, 512)),
             nn.LayerNorm(512),
             nn.ReLU(),
         )
         self.lstm = nn.LSTM(512, 128)
-        self.head = nn.Linear(128, env.single_action_space.n)
+        for name, param in self.lstm.named_parameters():
+            if "bias" in name:
+                nn.init.constant_(param, 0)
+            elif "weight" in name:
+                nn.init.orthogonal_(param, 1.0)
+        self.q_func = layer_init(nn.Linear(128, env.single_action_space.n))
 
-    
     def get_states(self, x, lstm_state, done):
         hidden = self.network(x / 255.0)
 
@@ -153,7 +161,7 @@ def get_states(self, x, lstm_state, done):
 
     def forward(self, x, lstm_state, done):
         hidden, lstm_state = self.get_states(x, lstm_state, done)
-        return self.head(hidden), lstm_state
+        return self.q_func(hidden), lstm_state
 
 
 def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
@@ -209,7 +217,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     assert isinstance(envs.action_space, gym.spaces.Discrete), "only discrete action space is supported"
 
     q_network = QNetwork(envs).to(device)
-    optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate)
+    optimizer = optim.RAdam(q_network.parameters(), lr=args.learning_rate)
 
     # ALGO Logic: Storage setup
     obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
@@ -219,7 +227,6 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     values = torch.zeros((args.num_steps, args.num_envs)).to(device)
     avg_returns = deque(maxlen=20)
 
-
     # TRY NOT TO MODIFY: start the game
     global_step = 0
     start_time = time.time()
diff --git a/docs/rl-algorithms/pqn.md b/docs/rl-algorithms/pqn.md
new file mode 100644
index 000000000..56de552e2
--- /dev/null
+++ b/docs/rl-algorithms/pqn.md
@@ -0,0 +1,236 @@
+# Parallel Q Network (PQN)
+
+
+## Overview
+
+PQN is a parallelized version of the Deep Q-learning algorithm. It is designed to be more efficient than DQN by using multiple agents to interact with the environment in parallel. PQN can be thought of as DQN (1) without replay buffer and target networks, and (2) with layer normalizations and parallel environments.
+
+Original paper: 
+
+* [Simplifying Deep Temporal Difference Learning](https://arxiv.org/html/2407.04811v2)
+
+Reference resources:
+
+* :material-github: [purejaxql](https://github.com/mttga/purejaxql)
+
+## Implemented Variants
+
+
+| Variants Implemented      | Description |
+| ----------- | ----------- |
+| :material-github: [`pqn.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/pqn.py), :material-file-document: [docs](/rl-algorithms/pqn/#pqnpy) | For classic control tasks like `CartPole-v1`. |
+| :material-github: [`pqn_atari_envpool.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/pqn_atari_envpool.py), :material-file-document: [docs](/rl-algorithms/pqn/#pqn_atari_envpoolpy) |  For Atari games. Uses the blazing fast Envpool Atari vectorized environment. It uses convolutional layers and common atari-based pre-processing techniques. |
+| :material-github: [`pqn_atari_envpool_lstm.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/pqn_atari_envpool_lstm.py), :material-file-document: [docs](/rl-algorithms/pqn/#pqn_atari_envpool_lstmpy) | For Atari games. Uses the blazing fast Envpool Atari vectorized environment. Using LSTM without stacked frames. |
+
+Below are our single-file implementations of PQN:
+
+## `pqn.py`
+
+The [pqn.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/pqn.py) has the following features:
+
+* Works with the `Box` observation space of low-level features
+* Works with the `Discrete` action space
+* Works with envs like `CartPole-v1`
+
+### Usage
+
+=== "poetry"
+
+    ```bash
+    poetry install
+    poetry run python cleanrl/pqn.py --help
+    poetry run python cleanrl/pqn.py --env-id CartPole-v1
+    ```
+
+=== "pip"
+
+    ```bash
+    python cleanrl/pqn.py --help
+    python cleanrl/pqn.py --env-id CartPole-v1
+    ```
+
+### Explanation of the logged metrics
+
+Running `python cleanrl/pqn.py` will automatically record various metrics such as actor or value losses in Tensorboard. Below is the documentation for these metrics:
+
+* `charts/episodic_return`: episodic return of the game
+* `charts/episodic_length`: episodic length of the game
+* `charts/SPS`: number of steps per second
+* `charts/learning_rate`: the current learning rate
+* `losses/td_loss`: the mean squared error (MSE) between the Q values at timestep $t$ and the Bellman update target estimated using the $Q(\lambda)$ returns.
+* `losses/q_values`: implemented as `qf1(data.observations, data.actions).view(-1)`, it is the average Q values of the sampled data in the replay buffer; useful when gauging if under or over estimation happens.
+
+### Implementation details
+
+1. Vectorized architecture (:material-github: [common/cmd_util.py#L22](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/common/cmd_util.py#L22))
+2. Orthogonal Initialization of Weights and Constant Initialization of biases (:material-github: [a2c/utils.py#L58)](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/a2c/utils.py#L58))
+3. Normalized Q Network (:material-github: [purejaxql/pqn_atari.py#L200](https://github.com/mttga/purejaxql/blob/2205ae5308134d2cedccd749074bff2871832dc8/purejaxql/pqn_atari.py#L200))
+4. Uses the RAdam Optimizer with the default epsilon parameter(:material-github: [purejaxql/pqn_atari.py#L362](https://github.com/mttga/purejaxql/blob/2205ae5308134d2cedccd749074bff2871832dc8/purejaxql/pqn_atari.py#L362))
+5. Adam Learning Rate Annealing (:material-github: [pqn2/pqn2.py#L133-L135](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/pqn2/pqn2.py#L133-L135))
+6. Q Lambda Returns (:material-github: [purejaxql/pqn_atari.py#L446](https://github.com/mttga/purejaxql/blob/2205ae5308134d2cedccd749074bff2871832dc8/purejaxql/pqn_atari.py#L446))
+7. Mini-batch Updates (:material-github: [pqn2/pqn2.py#L157-L166](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/pqn2/pqn2.py#L157-L166))
+8. Global Gradient Clipping (:material-github: [purejaxql/pqn_atari.py#L360](https://github.com/mttga/purejaxql/blob/2205ae5308134d2cedccd749074bff2871832dc8/purejaxql/pqn_atari.py#L360))
+
+### Experiment results
+
+To run benchmark experiments, see :material-github: [benchmark/pqn.sh](https://github.com/vwxyzjn/cleanrl/blob/master/benchmark/pqn.sh). Specifically, execute the following command:
+
+``` title="benchmark/pqn.sh" linenums="1"
+--8<-- "benchmark/pqn.sh:3:8"
+```
+
+# TODO
+
+Learning curves:
+
+``` title="benchmark/pqn_plot.sh" linenums="1"
+--8<-- "benchmark/pqn_plot.sh::9"
+```
+
+# TODO
+
+## `pqn_atari_envpool.py`
+
+The [pqn_atari_envpool.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/pqn_atari_envpool.py) has the following features:
+
+* Uses the blazing fast [Envpool](https://github.com/sail-sg/envpool) vectorized environment.
+* For Atari games. It uses convolutional layers and common atari-based pre-processing techniques.
+* Works with the Atari's pixel `Box` observation space of shape `(210, 160, 3)`
+* Works with the `Discrete` action space
+
+???+ warning
+
+    Note that `pqn_atari_envpool.py` does not work in Windows :fontawesome-brands-windows: and MacOs :fontawesome-brands-apple:. See envpool's built wheels here: [https://pypi.org/project/envpool/#files](https://pypi.org/project/envpool/#files)
+
+???+ bug
+
+    EnvPool's vectorized environment **does not behave the same** as gym's vectorized environment, which causes a compatibility bug in our PQN implementation. When an action $a$ results in an episode termination or truncation, the environment generates $s_{last}$ as the terminated or truncated state; we then use $s_{new}$ to denote the initial state of the new episodes. Here is how the bahviors differ:
+
+    * Under the vectorized environment of `envpool<=0.6.4`, the `obs` in `obs, reward, done, info = env.step(action)` is the truncated state $s_{last}$
+    * Under the vectorized environment of `gym==0.23.1`, the `obs` in `obs, reward, done, info = env.step(action)` is the initial state $s_{new}$.
+
+    This causes the $s_{last}$ to be off by one. 
+    See [:material-github: sail-sg/envpool#194](https://github.com/sail-sg/envpool/issues/194) for more detail. However, it does not seem to impact performance, so we take a note here and await for the upstream fix.
+
+
+### Usage
+
+=== "poetry"
+
+    ```bash
+    poetry install -E envpool
+    poetry run python cleanrl/pqn_atari_envpool.py --help
+    poetry run python cleanrl/pqn_atari_envpool.py --env-id Breakout-v5
+    ```
+
+=== "pip"
+
+    ```bash
+    pip install -r requirements/requirements-envpool.txt
+    python cleanrl/pqn_atari_envpool.py --help
+    python cleanrl/pqn_atari_envpool.py --env-id Breakout-v5
+    ```
+
+### Explanation of the logged metrics
+
+See [related docs](/rl-algorithms/pqn/#explanation-of-the-logged-metrics) for `pqn.py`.
+
+### Implementation details
+
+[pqn_atari_envpool.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/pqn_atari_envpool.py) uses a customized `RecordEpisodeStatistics` to work with envpool but has the same other implementation details as `ppo_atari.py`.
+
+### Experiment results
+
+To run benchmark experiments, see :material-github: [benchmark/pqn.sh](https://github.com/vwxyzjn/cleanrl/blob/master/benchmark/pqn.sh). Specifically, execute the following command:
+
+
+``` title="benchmark/pqn.sh" linenums="1"
+--8<-- "benchmark/pqn.sh:58:63"
+```
+
+# TODO
+
+
+Learning curves:
+
+``` title="benchmark/pqn_plot.sh" linenums="1"
+--8<-- "benchmark/pqn_plot.sh:51:62"
+```
+# TODO
+
+## `pqn_atari_envpool_lstm.py`
+
+The [pqn_atari_envpool_lstm.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/pqn_atari_envpool_lstm.py) has the following features:
+
+* Uses the blazing fast [Envpool](https://github.com/sail-sg/envpool) vectorized environment.
+* For Atari games using LSTM without stacked frames. It uses convolutional layers and common atari-based pre-processing techniques.
+* Works with the Atari's pixel `Box` observation space of shape `(210, 160, 3)`
+* Works with the `Discrete` action space
+
+???+ warning
+
+    Note that `pqn_atari_envpool.py` does not work in Windows :fontawesome-brands-windows: and MacOs :fontawesome-brands-apple:. See envpool's built wheels here: [https://pypi.org/project/envpool/#files](https://pypi.org/project/envpool/#files)
+
+???+ bug
+
+    EnvPool's vectorized environment **does not behave the same** as gym's vectorized environment, which causes a compatibility bug in our PQN implementation. When an action $a$ results in an episode termination or truncation, the environment generates $s_{last}$ as the terminated or truncated state; we then use $s_{new}$ to denote the initial state of the new episodes. Here is how the bahviors differ:
+
+    * Under the vectorized environment of `envpool<=0.6.4`, the `obs` in `obs, reward, done, info = env.step(action)` is the truncated state $s_{last}$
+    * Under the vectorized environment of `gym==0.23.1`, the `obs` in `obs, reward, done, info = env.step(action)` is the initial state $s_{new}$.
+
+    This causes the $s_{last}$ to be off by one. 
+    See [:material-github: sail-sg/envpool#194](https://github.com/sail-sg/envpool/issues/194) for more detail. However, it does not seem to impact performance, so we take a note here and await for the upstream fix.
+
+### Usage
+
+
+=== "poetry"
+
+    ```bash
+    poetry install -E atari
+    poetry run python cleanrl/pqn_atari_envpool_lstm.py --help
+    poetry run python cleanrl/pqn_atari_envpool_lstm.py --env-id Breakout-v5
+    ```
+
+=== "pip"
+
+    ```bash
+    pip install -r requirements/requirements-atari.txt
+    python cleanrl/pqn_atari_envpool_lstm.py --help
+    python cleanrl/pqn_atari_envpool_lstm.py --env-id Breakout-v5
+    ```
+
+
+### Explanation of the logged metrics
+
+See [related docs](/rl-algorithms/pqn/#explanation-of-the-logged-metrics) for `pqn.py`.
+
+### Implementation details
+
+[pqn_atari_envpool_lstm.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/pqn_atari_envpool_lstm.py) is based on the "5 LSTM implementation details" in [The 37 Implementation Details of Proximal Policy Optimization](https://iclr-blog-track.github.io/2022/03/25/pqn-implementation-details/), which are as follows:
+
+1. Layer initialization for LSTM layers (:material-github: [a2c/utils.py#L84-L86](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/a2c/utils.py#L84-L86))
+2. Initialize the LSTM states to be zeros (:material-github: [common/models.py#L179](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/common/models.py#L179))
+3. Reset LSTM states at the end of the episode (:material-github: [common/models.py#L141](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/common/models.py#L141))
+4. Prepare sequential rollouts in mini-batches (:material-github: [a2c/utils.py#L81](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/a2c/utils.py#L81))
+5. Reconstruct LSTM states during training (:material-github: [a2c/utils.py#L81](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/a2c/utils.py#L81))
+
+To help test out the memory, we remove the 4 stacked frames from the observation (i.e., using `env = gym.wrappers.FrameStack(env, 1)` instead of `env = gym.wrappers.FrameStack(env, 4)` like in `ppo_atari.py` )
+
+### Experiment results
+
+To run benchmark experiments, see :material-github: [benchmark/pqn.sh](https://github.com/vwxyzjn/cleanrl/blob/master/benchmark/pqn.sh). Specifically, execute the following command:
+
+``` title="benchmark/pqn.sh" linenums="1"
+--8<-- "benchmark/pqn.sh:47:52"
+```
+
+# TODO
+
+Learning curves:
+
+``` title="benchmark/pqn_plot.sh" linenums="1"
+--8<-- "benchmark/pqn_plot.sh:11:19"
+```
+
+# TODO
\ No newline at end of file

From c8acf70b4887914741e487a1de517795d6fd323b Mon Sep 17 00:00:00 2001
From: roger-creus <creus99@gmail.com>
Date: Thu, 31 Oct 2024 12:46:26 -0400
Subject: [PATCH 8/8] Added coumentation. Fixed links to the docs for PQN

---
 cleanrl/pqn.py                       |   2 +-
 cleanrl/pqn_atari_envpool.py         |   2 +-
 cleanrl/pqn_atari_envpool_lstm.py    |  10 ++--
 docs/rl-algorithms/pqn.md            |  86 ++++++++++++++++++---------
 docs/rl-algorithms/pqn/pqn.png       | Bin 0 -> 137314 bytes
 docs/rl-algorithms/pqn/pqn_lstm.png  | Bin 0 -> 126701 bytes
 docs/rl-algorithms/pqn/pqn_state.png | Bin 0 -> 61105 bytes
 mkdocs.yml                           |   1 +
 8 files changed, 67 insertions(+), 34 deletions(-)
 create mode 100644 docs/rl-algorithms/pqn/pqn.png
 create mode 100644 docs/rl-algorithms/pqn/pqn_lstm.png
 create mode 100644 docs/rl-algorithms/pqn/pqn_state.png

diff --git a/cleanrl/pqn.py b/cleanrl/pqn.py
index 2f9874803..6ed6e205d 100644
--- a/cleanrl/pqn.py
+++ b/cleanrl/pqn.py
@@ -1,4 +1,4 @@
-# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/dqn/#dqnpy
+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/pqn/#pqnpy
 import os
 import random
 import time
diff --git a/cleanrl/pqn_atari_envpool.py b/cleanrl/pqn_atari_envpool.py
index f1d7deb3d..45fd5a4cf 100644
--- a/cleanrl/pqn_atari_envpool.py
+++ b/cleanrl/pqn_atari_envpool.py
@@ -1,4 +1,4 @@
-# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_envpoolpy
+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/pqn/#pqn_atari_envpoolpy
 import os
 import random
 import time
diff --git a/cleanrl/pqn_atari_envpool_lstm.py b/cleanrl/pqn_atari_envpool_lstm.py
index 3b21d5cff..6b348b0af 100644
--- a/cleanrl/pqn_atari_envpool_lstm.py
+++ b/cleanrl/pqn_atari_envpool_lstm.py
@@ -1,4 +1,4 @@
-# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_envpoolpy
+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/pqn/#pqn_atari_envpool_lstmpy
 import os
 import random
 import time
@@ -15,6 +15,7 @@
 import tyro
 from torch.utils.tensorboard import SummaryWriter
 
+
 @dataclass
 class Args:
     exp_name: str = os.path.basename(__file__)[: -len(".py")]
@@ -168,6 +169,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     slope = (end_e - start_e) / duration
     return max(slope * t + start_e, end_e)
 
+
 if __name__ == "__main__":
     args = tyro.cli(Args)
     args.batch_size = int(args.num_envs * args.num_steps)
@@ -232,7 +234,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     start_time = time.time()
     next_obs = torch.Tensor(envs.reset()).to(device)
     next_done = torch.zeros(args.num_envs).to(device)
-    
+
     next_lstm_state = (
         torch.zeros(q_network.lstm.num_layers, args.num_envs, q_network.lstm.hidden_size).to(device),
         torch.zeros(q_network.lstm.num_layers, args.num_envs, q_network.lstm.hidden_size).to(device),
@@ -260,7 +262,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
                 max_actions = torch.argmax(q_values, dim=1)
                 values[step] = q_values[torch.arange(args.num_envs), max_actions].flatten()
 
-            explore = (torch.rand((args.num_envs,)).to(device) < epsilon)
+            explore = torch.rand((args.num_envs,)).to(device) < epsilon
             action = torch.where(explore, random_actions, max_actions)
             actions[step] = action
 
@@ -315,7 +317,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
                 old_val, _ = q_network(
                     b_obs[mb_inds],
                     (initial_lstm_state[0][:, mbenvinds], initial_lstm_state[1][:, mbenvinds]),
-                    b_dones[mb_inds]
+                    b_dones[mb_inds],
                 )
                 old_val = old_val.gather(1, b_actions[mb_inds].unsqueeze(-1).long()).squeeze()
 
diff --git a/docs/rl-algorithms/pqn.md b/docs/rl-algorithms/pqn.md
index 56de552e2..3f8fafb46 100644
--- a/docs/rl-algorithms/pqn.md
+++ b/docs/rl-algorithms/pqn.md
@@ -58,7 +58,7 @@ Running `python cleanrl/pqn.py` will automatically record various metrics such a
 * `charts/SPS`: number of steps per second
 * `charts/learning_rate`: the current learning rate
 * `losses/td_loss`: the mean squared error (MSE) between the Q values at timestep $t$ and the Bellman update target estimated using the $Q(\lambda)$ returns.
-* `losses/q_values`: implemented as `qf1(data.observations, data.actions).view(-1)`, it is the average Q values of the sampled data in the replay buffer; useful when gauging if under or over estimation happens.
+* `losses/q_values`: it is the average Q values of the sampled data in the replay buffer; useful when gauging if under or over estimation happens.
 
 ### Implementation details
 
@@ -73,21 +73,29 @@ Running `python cleanrl/pqn.py` will automatically record various metrics such a
 
 ### Experiment results
 
-To run benchmark experiments, see :material-github: [benchmark/pqn.sh](https://github.com/vwxyzjn/cleanrl/blob/master/benchmark/pqn.sh). Specifically, execute the following command:
+Episode Rewards:
 
-``` title="benchmark/pqn.sh" linenums="1"
---8<-- "benchmark/pqn.sh:3:8"
-```
+| Environment      | CleanRL PQN       |
+|------------------|-------------------|
+| CartPole-v1      | 408.14 ± 128.42   |
+| Acrobot-v1       | -93.71 ± 2.94     |
+| MountainCar-v0   | -200.00 ± 0.00    |
 
-# TODO
+Runtime:
+
+| Environment      | CleanRL PQN          |
+|------------------|----------------------|
+| CartPole-v1      | 3.619667511995135    |
+| Acrobot-v1       | 4.264845468334595    |
+| MountainCar-v0   | 3.99800178870078     |
 
 Learning curves:
 
-``` title="benchmark/pqn_plot.sh" linenums="1"
---8<-- "benchmark/pqn_plot.sh::9"
-```
+<img src="../pqn/pqn_state.png">
+
+Tracked experiments: 
 
-# TODO
+<iframe src="https://api.wandb.ai/links/rogercreus/9s50xw0j" style="width:100%; height:500px" title="CleanRL-s-PPO-TrXL"></iframe>
 
 ## `pqn_atari_envpool.py`
 
@@ -141,22 +149,34 @@ See [related docs](/rl-algorithms/pqn/#explanation-of-the-logged-metrics) for `p
 
 ### Experiment results
 
-To run benchmark experiments, see :material-github: [benchmark/pqn.sh](https://github.com/vwxyzjn/cleanrl/blob/master/benchmark/pqn.sh). Specifically, execute the following command:
+Episode Rewards:
 
+| Environment       | CleanRL PQN        |
+|-------------------|--------------------|
+| Breakout-v5       | 356.93 ± 7.48      |
+| SpaceInvaders-v5  | 900.07 ± 107.95    |
+| BeamRider-v5      | 1987.97 ± 24.47    |
+| Pong-v5           | 20.44 ± 0.11       |
+| MsPacman-v5       | 2437.57 ± 215.01   |
 
-``` title="benchmark/pqn.sh" linenums="1"
---8<-- "benchmark/pqn.sh:58:63"
-```
+Runtime:
 
-# TODO
+| Environment       | CleanRL PQN           |
+|-------------------|-----------------------|
+| Breakout-v5       | 41.27235000576079     |
+| SpaceInvaders-v5  | 42.191246278536035    |
+| BeamRider-v5      | 42.66799268151052     |
+| Pong-v5           | 39.35770012905844     |
+| MsPacman-v5       | 43.22808379473344     |
 
 
 Learning curves:
 
-``` title="benchmark/pqn_plot.sh" linenums="1"
---8<-- "benchmark/pqn_plot.sh:51:62"
-```
-# TODO
+<img src="../pqn/pqn.png">
+
+Tracked experiments: 
+
+<iframe src="https://wandb.ai/rogercreus/cleanRL/reports/PQN-PR-472--Vmlldzo5ODg1NTkx" style="width:100%; height:500px" title="CleanRL-s-PPO-TrXL"></iframe>
 
 ## `pqn_atari_envpool_lstm.py`
 
@@ -219,18 +239,28 @@ To help test out the memory, we remove the 4 stacked frames from the observation
 
 ### Experiment results
 
-To run benchmark experiments, see :material-github: [benchmark/pqn.sh](https://github.com/vwxyzjn/cleanrl/blob/master/benchmark/pqn.sh). Specifically, execute the following command:
+Episode Rewards:
+
+| Environment       | CleanRL PQN        |
+|-------------------|--------------------|
+| Breakout-v5       | 366.47 ± 2.72      |
+| SpaceInvaders-v5  | 681.92 ± 40.15     |
+| BeamRider-v5      | 2050.85 ± 38.58    |
+| MsPacman-v5       | 1815.20 ± 183.03   |
 
-``` title="benchmark/pqn.sh" linenums="1"
---8<-- "benchmark/pqn.sh:47:52"
-```
+Runtime:
 
-# TODO
+| Environment       | CleanRL PQN           |
+|-------------------|-----------------------|
+| Breakout-v5       | 170.30230232607076    |
+| SpaceInvaders-v5  | 168.45747969698144    |
+| BeamRider-v5      | 172.11561139317593    |
+| MsPacman-v5       | 171.66131707108408    |
 
 Learning curves:
 
-``` title="benchmark/pqn_plot.sh" linenums="1"
---8<-- "benchmark/pqn_plot.sh:11:19"
-```
+<img src="../pqn/pqn_lstm.png">
+
+Tracked experiments: 
 
-# TODO
\ No newline at end of file
+<iframe src="https://wandb.ai/rogercreus/cleanRL/reports/PQN-PR-472--Vmlldzo5ODg1NTkx" style="width:100%; height:500px" title="CleanRL-s-PPO-TrXL"></iframe>
\ No newline at end of file
diff --git a/docs/rl-algorithms/pqn/pqn.png b/docs/rl-algorithms/pqn/pqn.png
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diff --git a/mkdocs.yml b/mkdocs.yml
index 9c8c97695..6f0bd2255 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -50,6 +50,7 @@ nav:
     - rl-algorithms/rpo.md
     - rl-algorithms/qdagger.md
     - rl-algorithms/ppo-trxl.md
+    - rl-algorithms/pqn.md
   - Advanced:
     - advanced/hyperparameter-tuning.md
     - advanced/resume-training.md