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ppo_continuous_action_envpool.py
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ppo_continuous_action_envpool.py
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# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_continuous_actionpy
import argparse
import os
import random
import time
from collections import deque
from distutils.util import strtobool
import envpool
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions.normal import Normal
from torch.utils.tensorboard import SummaryWriter
def parse_args():
# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
help="the name of this experiment")
parser.add_argument("--seed", type=int, default=1,
help="seed of the experiment")
parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="if toggled, `torch.backends.cudnn.deterministic=False`")
parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="if toggled, cuda will be enabled by default")
parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="if toggled, this experiment will be tracked with Weights and Biases")
parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
help="the wandb's project name")
parser.add_argument("--wandb-entity", type=str, default=None,
help="the entity (team) of wandb's project")
parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="weather to capture videos of the agent performances (check out `videos` folder)")
# Algorithm specific arguments
parser.add_argument("--env-id", type=str, default="HalfCheetah-v4",
help="the id of the environment")
parser.add_argument("--total-timesteps", type=int, default=10000000,
help="total timesteps of the experiments")
parser.add_argument("--learning-rate", type=float, default=0.00295,
help="the learning rate of the optimizer")
parser.add_argument("--num-envs", type=int, default=64,
help="the number of parallel game environments")
parser.add_argument("--num-steps", type=int, default=64,
help="the number of steps to run in each environment per policy rollout")
parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="Toggle learning rate annealing for policy and value networks")
parser.add_argument("--gae", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="Use GAE for advantage computation")
parser.add_argument("--gamma", type=float, default=0.99,
help="the discount factor gamma")
parser.add_argument("--gae-lambda", type=float, default=0.95,
help="the lambda for the general advantage estimation")
parser.add_argument("--num-minibatches", type=int, default=4,
help="the number of mini-batches")
parser.add_argument("--update-epochs", type=int, default=2,
help="the K epochs to update the policy")
parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="Toggles advantages normalization")
parser.add_argument("--clip-coef", type=float, default=0.2,
help="the surrogate clipping coefficient")
parser.add_argument("--clip-vloss", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="Toggles whether or not to use a clipped loss for the value function, as per the paper.")
parser.add_argument("--ent-coef", type=float, default=0.0,
help="coefficient of the entropy")
parser.add_argument("--vf-coef", type=float, default=1.3,
help="coefficient of the value function")
parser.add_argument("--max-grad-norm", type=float, default=3.5,
help="the maximum norm for the gradient clipping")
parser.add_argument("--target-kl", type=float, default=None,
help="the target KL divergence threshold")
args = parser.parse_args()
args.batch_size = int(args.num_envs * args.num_steps)
args.minibatch_size = int(args.batch_size // args.num_minibatches)
# fmt: on
return args
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.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 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 Agent(nn.Module):
def __init__(self, envs):
super().__init__()
self.critic = nn.Sequential(
layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
nn.Tanh(),
layer_init(nn.Linear(64, 64)),
nn.Tanh(),
layer_init(nn.Linear(64, 1), std=1.0),
)
self.actor_mean = nn.Sequential(
layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
nn.Tanh(),
layer_init(nn.Linear(64, 64)),
nn.Tanh(),
layer_init(nn.Linear(64, np.prod(envs.single_action_space.shape)), std=0.01),
)
self.actor_logstd = nn.Parameter(torch.zeros(1, np.prod(envs.single_action_space.shape)))
def get_value(self, x):
return self.critic(x)
def get_action_and_value(self, x, action=None):
action_mean = self.actor_mean(x)
action_logstd = self.actor_logstd.expand_as(action_mean)
action_std = torch.exp(action_logstd)
probs = Normal(action_mean, action_std)
if action is None:
action = probs.sample()
return action, probs.log_prob(action).sum(1), probs.entropy().sum(1), self.critic(x)
if __name__ == "__main__":
args = parse_args()
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,
)
envs.num_envs = args.num_envs
envs.single_action_space = envs.action_space
envs.single_observation_space = envs.observation_space
envs.is_vector_env = True
envs = RecordEpisodeStatistics(envs)
envs = gym.wrappers.ClipAction(envs)
envs = gym.wrappers.NormalizeObservation(envs)
envs = gym.wrappers.TransformObservation(envs, lambda obs: np.clip(obs, -10, 10))
envs = gym.wrappers.NormalizeReward(envs)
envs = gym.wrappers.TransformReward(envs, lambda reward: np.clip(reward, -10, 10))
assert isinstance(envs.single_action_space, gym.spaces.Box), "only continuous action space is supported"
agent = Agent(envs).to(device)
optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
# 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)
logprobs = torch.zeros((args.num_steps, args.num_envs)).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)
num_updates = args.total_timesteps // args.batch_size
for update in range(1, num_updates + 1):
# Annealing the rate if instructed to do so.
if args.anneal_lr:
frac = 1.0 - (update - 1.0) / num_updates
lrnow = frac * args.learning_rate
optimizer.param_groups[0]["lr"] = lrnow
for step in range(0, args.num_steps):
global_step += 1 * args.num_envs
obs[step] = next_obs
dones[step] = next_done
# ALGO LOGIC: action logic
with torch.no_grad():
action, logprob, _, value = agent.get_action_and_value(next_obs)
values[step] = value.flatten()
actions[step] = action
logprobs[step] = logprob
# TRY NOT TO MODIFY: execute the game and log data.
next_obs, reward, 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(done).to(device)
for idx, d in enumerate(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():
next_value = agent.get_value(next_obs).reshape(1, -1)
if args.gae:
advantages = torch.zeros_like(rewards).to(device)
lastgaelam = 0
for t in reversed(range(args.num_steps)):
if t == args.num_steps - 1:
nextnonterminal = 1.0 - next_done
nextvalues = next_value
else:
nextnonterminal = 1.0 - dones[t + 1]
nextvalues = values[t + 1]
delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
returns = advantages + values
else:
returns = torch.zeros_like(rewards).to(device)
for t in reversed(range(args.num_steps)):
if t == args.num_steps - 1:
nextnonterminal = 1.0 - next_done
next_return = next_value
else:
nextnonterminal = 1.0 - dones[t + 1]
next_return = returns[t + 1]
returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
advantages = returns - values
# flatten the batch
b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
b_logprobs = logprobs.reshape(-1)
b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
b_advantages = advantages.reshape(-1)
b_returns = returns.reshape(-1)
b_values = values.reshape(-1)
# Optimizing the policy and value network
b_inds = np.arange(args.batch_size)
clipfracs = []
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]
_, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions[mb_inds])
logratio = newlogprob - b_logprobs[mb_inds]
ratio = logratio.exp()
with torch.no_grad():
# calculate approx_kl http://joschu.net/blog/kl-approx.html
old_approx_kl = (-logratio).mean()
approx_kl = ((ratio - 1) - logratio).mean()
clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]
mb_advantages = b_advantages[mb_inds]
if args.norm_adv:
mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
# Policy loss
pg_loss1 = -mb_advantages * ratio
pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef)
pg_loss = torch.max(pg_loss1, pg_loss2).mean()
# Value loss
newvalue = newvalue.view(-1)
if args.clip_vloss:
v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
v_clipped = b_values[mb_inds] + torch.clamp(
newvalue - b_values[mb_inds],
-args.clip_coef,
args.clip_coef,
)
v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
v_loss = 0.5 * v_loss_max.mean()
else:
v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()
entropy_loss = entropy.mean()
loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
optimizer.step()
if args.target_kl is not None:
if approx_kl > args.target_kl:
break
y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
var_y = np.var(y_true)
explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
# TRY NOT TO MODIFY: record rewards for plotting purposes
writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step)
writer.add_scalar("losses/entropy", entropy_loss.item(), global_step)
writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step)
writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step)
writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step)
writer.add_scalar("losses/explained_variance", explained_var, 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()