[Refactor] Deprecate interaction_mode

docs/source/reference/collectors.rst

@@ -54,7 +54,7 @@ Besides those compute parameters, users may choose to configure the following pa
 - reset_at_each_iter: if :obj:`True`, the environment(s) will be reset after each batch collection
 - split_trajs: if :obj:`True`, the trajectories will be split and delivered in a padded tensordict
   along with a :obj:`"mask"` key that will point to a boolean mask representing the valid values.
-- exploration_mode: the exploration strategy to be used with the policy.
+- exploration_type: the exploration strategy to be used with the policy.
 - reset_when_done: whether environments should be reset when reaching a done state.
 
 

examples/a2c/a2c.py

@@ -9,7 +9,7 @@
 import torch.cuda
 from hydra.core.config_store import ConfigStore
 from torchrl.envs.transforms import RewardScaling
-from torchrl.envs.utils import set_exploration_mode
+from torchrl.envs.utils import ExplorationType, set_exploration_type
 from torchrl.objectives.value import TD0Estimator
 from torchrl.record.loggers import generate_exp_name, get_logger
 from torchrl.trainers.helpers.collectors import (
@@ -95,7 +95,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
 
     loss_module = make_a2c_loss(model, cfg)
     if cfg.gSDE:
-        with torch.no_grad(), set_exploration_mode("random"):
+        with torch.no_grad(), set_exploration_type(ExplorationType.RANDOM):
             # get dimensions to build the parallel env
             proof_td = model(proof_env.reset().to(device))
         action_dim_gsde, state_dim_gsde = proof_td.get("_eps_gSDE").shape[-2:]

examples/bandits/dqn.py

@@ -10,7 +10,7 @@
 from torch import nn
 
 from torchrl.envs.libs.openml import OpenMLEnv
-from torchrl.envs.utils import set_exploration_mode
+from torchrl.envs.utils import ExplorationType, set_exploration_type
 from torchrl.modules import DistributionalQValueActor, EGreedyWrapper, MLP, QValueActor
 from torchrl.objectives import DistributionalDQNLoss, DQNLoss
 
@@ -94,7 +94,7 @@
     init_r = None
     init_loss = None
     for i in pbar:
-        with set_exploration_mode("random"):
+        with set_exploration_type(ExplorationType.RANDOM):
             data = env.step(policy(env.reset()))
         loss_vals = loss(data)
         loss_val = sum(

examples/ddpg/ddpg.py

@@ -10,7 +10,7 @@
 from hydra.core.config_store import ConfigStore
 from torchrl.envs import EnvCreator, ParallelEnv
 from torchrl.envs.transforms import RewardScaling, TransformedEnv
-from torchrl.envs.utils import set_exploration_mode
+from torchrl.envs.utils import ExplorationType, set_exploration_type
 from torchrl.modules import OrnsteinUhlenbeckProcessWrapper
 from torchrl.record import VideoRecorder
 from torchrl.record.loggers import generate_exp_name, get_logger
@@ -122,7 +122,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
         actor_model_explore.share_memory()
 
     if cfg.gSDE:
-        with torch.no_grad(), set_exploration_mode("random"):
+        with torch.no_grad(), set_exploration_type(ExplorationType.RANDOM):
             # get dimensions to build the parallel env
             proof_td = actor_model_explore(proof_env.reset().to(device))
         action_dim_gsde, state_dim_gsde = proof_td.get("_eps_gSDE").shape[-2:]

examples/discrete_sac/discrete_sac.py

@@ -9,6 +9,7 @@
 import torch
 import torch.cuda
 import tqdm
+from tensordict.nn import InteractionType
 
 from torch import nn, optim
 from torchrl.collectors import SyncDataCollector
@@ -22,7 +23,7 @@
 from torchrl.envs import EnvCreator, ParallelEnv
 
 from torchrl.envs.libs.gym import GymEnv
-from torchrl.envs.utils import set_exploration_mode
+from torchrl.envs.utils import ExplorationType, set_exploration_type
 from torchrl.modules import MLP, SafeModule
 from torchrl.modules.distributions import OneHotCategorical
 
@@ -134,7 +135,7 @@ def env_factory(num_workers):
         out_keys=["action"],
         distribution_class=OneHotCategorical,
         distribution_kwargs={},
-        default_interaction_mode="random",
+        default_interaction_type=InteractionType.RANDOM,
         return_log_prob=False,
     ).to(device)
 
@@ -224,7 +225,7 @@ def env_factory(num_workers):
         new_collected_epochs = len(np.unique(tensordict["collector"]["traj_ids"]))
         if r0 is None:
             r0 = (
-                tensordict["reward"].sum().item()
+                tensordict["next", "reward"].sum().item()
                 / new_collected_epochs
                 / cfg.env_per_collector
             )
@@ -284,7 +285,7 @@ def env_factory(num_workers):
         rewards.append(
             (
                 i,
-                tensordict["reward"].sum().item()
+                tensordict["next", "reward"].sum().item()
                 / cfg.env_per_collector
                 / new_collected_epochs,
             )
@@ -307,16 +308,16 @@ def env_factory(num_workers):
                 }
             )
 
-        with set_exploration_mode(
-            "random"
+        with set_exploration_type(
+            ExplorationType.RANDOM
         ), torch.no_grad():  # TODO: exploration mode to mean causes nans
 
             eval_rollout = test_env.rollout(
                 max_steps=cfg.max_frames_per_traj,
                 policy=actor,
                 auto_cast_to_device=True,
             ).clone()
-            eval_reward = eval_rollout["reward"].sum(-2).mean().item()
+            eval_reward = eval_rollout["next", "reward"].sum(-2).mean().item()
             rewards_eval.append((i, eval_reward))
             eval_str = f"eval cumulative reward: {rewards_eval[-1][1]: 4.4f} (init: {rewards_eval[0][1]: 4.4f})"
             metrics.update({"test_reward": rewards_eval[-1][1]})

examples/distributed/collectors/multi_nodes/ray_train.py

@@ -196,7 +196,7 @@
                 optim.step()
                 optim.zero_grad()
 
-        logs["reward"].append(tensordict_data["reward"].mean().item())
+        logs["reward"].append(tensordict_data["next", "reward"].mean().item())
         pbar.update(tensordict_data.numel() * frame_skip)
         cum_reward_str = f"average reward={logs['reward'][-1]: 4.4f} (init={logs['reward'][0]: 4.4f})"
         logs["step_count"].append(tensordict_data["step_count"].max().item())
@@ -206,8 +206,10 @@
         with set_exploration_mode("mean"), torch.no_grad():
             # execute a rollout with the trained policy
             eval_rollout = env.rollout(1000, policy_module)
-            logs["eval reward"].append(eval_rollout["reward"].mean().item())
-            logs["eval reward (sum)"].append(eval_rollout["reward"].sum().item())
+            logs["eval reward"].append(eval_rollout["next", "reward"].mean().item())
+            logs["eval reward (sum)"].append(
+                eval_rollout["next", "reward"].sum().item()
+            )
             logs["eval step_count"].append(eval_rollout["step_count"].max().item())
             eval_str = f"eval cumulative reward: {logs['eval reward (sum)'][-1]: 4.4f} (init: {logs['eval reward (sum)'][0]: 4.4f}), eval step-count: {logs['eval step_count'][-1]}"
             del eval_rollout

examples/dreamer/dreamer.py

@@ -235,7 +235,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
         replay_buffer.extend(tensordict.cpu())
         logger.log_scalar(
             "r_training",
-            tensordict["reward"].mean().detach().item(),
+            tensordict["next", "reward"].mean().detach().item(),
             step=collected_frames,
         )
 

examples/iql/iql_online.py

@@ -20,7 +20,7 @@
 from torchrl.data.replay_buffers.storages import LazyMemmapStorage
 from torchrl.envs import EnvCreator, ParallelEnv
 from torchrl.envs.libs.gym import GymEnv
-from torchrl.envs.utils import set_exploration_mode
+from torchrl.envs.utils import ExplorationType, set_exploration_type
 from torchrl.modules import MLP, ProbabilisticActor, ValueOperator
 from torchrl.modules.distributions import TanhNormal
 
@@ -147,7 +147,7 @@ def env_factory(num_workers):
         module=actor_module,
         distribution_class=dist_class,
         distribution_kwargs=dist_kwargs,
-        default_interaction_mode="random",
+        default_interaction_type=ExplorationType.RANDOM,
         return_log_prob=False,
     )
 
@@ -247,7 +247,7 @@ def env_factory(num_workers):
         collector.update_policy_weights_()
 
         if r0 is None:
-            r0 = tensordict["reward"].sum(-1).mean().item()
+            r0 = tensordict["next", "reward"].sum(-1).mean().item()
         pbar.update(tensordict.numel())
 
         if "mask" in tensordict.keys():
@@ -293,7 +293,9 @@ def env_factory(num_workers):
             if cfg.prb:
                 replay_buffer.update_priority(sampled_tensordict)
 
-        rewards.append((i, tensordict["reward"].sum().item() / cfg.env_per_collector))
+        rewards.append(
+            (i, tensordict["next", "reward"].sum().item() / cfg.env_per_collector)
+        )
         train_log = {
             "train_reward": rewards[-1][1],
             "collected_frames": collected_frames,
@@ -309,13 +311,13 @@ def env_factory(num_workers):
         for key, value in train_log.items():
             logger.log_scalar(key, value, step=collected_frames)
 
-        with set_exploration_mode("mean"), torch.no_grad():
+        with set_exploration_type(ExplorationType.MEAN), torch.no_grad():
             eval_rollout = test_env.rollout(
                 max_steps=cfg.max_frames_per_traj,
                 policy=model[0],
                 auto_cast_to_device=True,
             ).clone()
-            eval_reward = eval_rollout["reward"].sum(-2).mean().item()
+            eval_reward = eval_rollout["next", "reward"].sum(-2).mean().item()
             rewards_eval.append((i, eval_reward))
             eval_str = f"eval cumulative reward: {rewards_eval[-1][1]: 4.4f} (init: {rewards_eval[0][1]: 4.4f})"
             logger.log_scalar("test_reward", rewards_eval[-1][1], step=collected_frames)

examples/ppo/ppo.py

@@ -10,7 +10,7 @@
 from hydra.core.config_store import ConfigStore
 from torchrl.envs import EnvCreator, ParallelEnv
 from torchrl.envs.transforms import RewardScaling, TransformedEnv
-from torchrl.envs.utils import set_exploration_mode
+from torchrl.envs.utils import ExplorationType, set_exploration_type
 from torchrl.objectives.value import GAE
 from torchrl.record import VideoRecorder
 from torchrl.record.loggers import generate_exp_name, get_logger
@@ -98,7 +98,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
 
     loss_module = make_ppo_loss(model, cfg)
     if cfg.gSDE:
-        with torch.no_grad(), set_exploration_mode("random"):
+        with torch.no_grad(), set_exploration_type(ExplorationType.RANDOM):
             # get dimensions to build the parallel env
             proof_td = model(proof_env.reset().to(device))
         action_dim_gsde, state_dim_gsde = proof_td.get("_eps_gSDE").shape[-2:]

examples/redq/redq.py

@@ -12,7 +12,7 @@
 from hydra.core.config_store import ConfigStore
 from torchrl.envs import EnvCreator, ParallelEnv
 from torchrl.envs.transforms import RewardScaling, TransformedEnv
-from torchrl.envs.utils import set_exploration_mode
+from torchrl.envs.utils import ExplorationType, set_exploration_type
 from torchrl.modules import OrnsteinUhlenbeckProcessWrapper
 from torchrl.record import VideoRecorder
 from torchrl.record.loggers import generate_exp_name, get_logger
@@ -134,7 +134,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
         actor_model_explore.share_memory()
 
     if cfg.gSDE:
-        with torch.no_grad(), set_exploration_mode("random"):
+        with torch.no_grad(), set_exploration_type(ExplorationType.RANDOM):
             # get dimensions to build the parallel env
             proof_td = actor_model_explore(proof_env.reset().to(device))
         action_dim_gsde, state_dim_gsde = proof_td.get("_eps_gSDE").shape[-2:]

examples/sac/sac.py

@@ -12,7 +12,7 @@
 from hydra.core.config_store import ConfigStore
 from torchrl.envs import EnvCreator, ParallelEnv
 from torchrl.envs.transforms import RewardScaling, TransformedEnv
-from torchrl.envs.utils import set_exploration_mode
+from torchrl.envs.utils import ExplorationType, set_exploration_type
 from torchrl.modules import OrnsteinUhlenbeckProcessWrapper
 from torchrl.record import VideoRecorder
 from torchrl.record.loggers import generate_exp_name, get_logger
@@ -132,7 +132,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
         actor_model_explore.share_memory()
 
     if cfg.gSDE:
-        with torch.no_grad(), set_exploration_mode("random"):
+        with torch.no_grad(), set_exploration_type(ExplorationType.RANDOM):
             # get dimensions to build the parallel env
             proof_td = actor_model_explore(proof_env.reset().to(device))
         action_dim_gsde, state_dim_gsde = proof_td.get("_eps_gSDE").shape[-2:]

examples/td3/td3.py

@@ -10,6 +10,7 @@
 import torch
 import torch.cuda
 import tqdm
+from tensordict.nn import InteractionType
 
 from torch import nn, optim
 from torchrl.collectors import MultiSyncDataCollector
@@ -26,7 +27,7 @@
 )
 from torchrl.envs.libs.gym import GymEnv
 from torchrl.envs.transforms import RewardScaling
-from torchrl.envs.utils import set_exploration_mode
+from torchrl.envs.utils import ExplorationType, set_exploration_type
 from torchrl.modules import (
     AdditiveGaussianWrapper,
     MLP,
@@ -168,7 +169,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
         module=actor_module,
         distribution_class=dist_class,
         distribution_kwargs=dist_kwargs,
-        default_interaction_mode="random",
+        default_interaction_type=InteractionType.RANDOM,
         return_log_prob=False,
     )
 
@@ -191,7 +192,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
     model = nn.ModuleList([actor, qvalue]).to(device)
 
     # init nets
-    with torch.no_grad(), set_exploration_mode("random"):
+    with torch.no_grad(), set_exploration_type(ExplorationType.RANDOM):
         td = eval_env.reset()
         td = td.to(device)
         for net in model:
@@ -272,7 +273,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
         collector.update_policy_weights_()
 
         if r0 is None:
-            r0 = tensordict["reward"].sum(-1).mean().item()
+            r0 = tensordict["next", "reward"].sum(-1).mean().item()
         pbar.update(tensordict.numel())
 
         # extend the replay buffer with the new data
@@ -321,7 +322,9 @@ def main(cfg: "DictConfig"):  # noqa: F821
                 if cfg.prb:
                     replay_buffer.update_priority(sampled_tensordict)
 
-        rewards.append((i, tensordict["reward"].sum().item() / cfg.env_per_collector))
+        rewards.append(
+            (i, tensordict["next", "reward"].sum().item() / cfg.env_per_collector)
+        )
         train_log = {
             "train_reward": rewards[-1][1],
             "collected_frames": collected_frames,
@@ -336,13 +339,13 @@ def main(cfg: "DictConfig"):  # noqa: F821
         for key, value in train_log.items():
             logger.log_scalar(key, value, step=collected_frames)
 
-        with set_exploration_mode("mean"), torch.no_grad():
+        with set_exploration_type(ExplorationType.MEAN), torch.no_grad():
             eval_rollout = eval_env.rollout(
                 cfg.max_frames_per_traj // cfg.frame_skip,
                 actor_model_explore,
                 auto_cast_to_device=True,
             )
-            eval_reward = eval_rollout["reward"].sum(-2).mean().item()
+            eval_reward = eval_rollout["next", "reward"].sum(-2).mean().item()
             rewards_eval.append((i, eval_reward))
             eval_str = f"eval cumulative reward: {rewards_eval[-1][1]: 4.4f} (init: {rewards_eval[0][1]: 4.4f})"
             logger.log_scalar("test_reward", rewards_eval[-1][1], step=collected_frames)

test/test_cost.py

@@ -7,6 +7,7 @@
 from copy import deepcopy
 
 from packaging import version as pack_version
+from tensordict.nn import InteractionType
 
 _has_functorch = True
 try:
@@ -3112,7 +3113,7 @@ def _create_actor_model(self, rssm_hidden_dim, state_dim, mlp_num_units=200):
             SafeProbabilisticModule(
                 in_keys=["loc", "scale"],
                 out_keys="action",
-                default_interaction_mode="random",
+                default_interaction_type=InteractionType.RANDOM,
                 distribution_class=TanhNormal,
             ),
         )