Merge branch 'main' into sac_benchmark

# Conflicts:
#	examples/ddpg/config.yaml
#	examples/ddpg/utils.py
#	examples/td3/config.yaml
#	examples/td3/utils.py

.github/unittest/linux_examples/scripts/run_test.sh

@@ -68,7 +68,6 @@ python .github/unittest/helpers/coverage_run_parallel.py examples/ddpg/ddpg.py \
   collector.init_random_frames=10 \
   optim.batch_size=10 \
   collector.frames_per_batch=16 \
-  collector.num_workers=4 \
   collector.env_per_collector=2 \
   collector.collector_device=cuda:0 \
   network.device=cuda:0 \
@@ -184,7 +183,6 @@ python .github/unittest/helpers/coverage_run_parallel.py examples/ddpg/ddpg.py \
   collector.init_random_frames=10 \
   optim.batch_size=10 \
   collector.frames_per_batch=16 \
-  collector.num_workers=2 \
   collector.env_per_collector=1 \
   collector.collector_device=cuda:0 \
   network.device=cuda:0 \

examples/ddpg/config.yaml

@@ -1,45 +1,47 @@
-# Environment
+# environment and task
 env:
   name: HalfCheetah-v3
   task: ""
-  exp_name: "HalfCheetah-DDPG"
-  library: gym
-  frame_skip: 1
-  seed: 1
+  exp_name: ${env.name}_DDPG
+  library: gymnasium
+  max_episode_steps: 1000
+  seed: 42
 
-# Collection
+# collector
 collector:
-  total_frames: 1000000
-  init_random_frames: 10000
+  total_frames: 1_000_000
+  init_random_frames: 25_000
   frames_per_batch: 1000
-  max_frames_per_traj: 1000
   init_env_steps: 1000
-  async_collection: 1
+  reset_at_each_iter: False
   collector_device: cpu
   env_per_collector: 1
-  num_workers: 1
 
-# Replay Buffer
+
+# replay buffer
 replay_buffer:
   size: 1000000
   prb: 0 # use prioritized experience replay
+  scratch_dir: ${env.exp_name}_${env.seed}
 
-# Optimization
+# optimization
 optim:
   utd_ratio: 1.0
   gamma: 0.99
-  loss_function: smooth_l1
-  lr: 3e-4
-  weight_decay: 2e-4
+  loss_function: l2
+  lr: 3.0e-4
+  weight_decay: 1e-4
   batch_size: 256
   target_update_polyak: 0.995
 
+# network
 network:
   hidden_sizes: [256, 256]
   activation: relu
   device: "cuda:0"
+  noise_type: "ou" # ou or gaussian
 
-# Logging
+# logging
 logger:
   backend: wandb
   mode: online

examples/ddpg/ddpg.py

@@ -11,15 +11,19 @@
 The helper functions are coded in the utils.py associated with this script.
 """
 
+import time
+
 import hydra
 
 import numpy as np
 import torch
 import torch.cuda
 import tqdm
+
 from torchrl.envs.utils import ExplorationType, set_exploration_type
 from torchrl.record.loggers import generate_exp_name, get_logger
 from utils import (
+    log_metrics,
     make_collector,
     make_ddpg_agent,
     make_environment,
@@ -33,6 +37,7 @@
 def main(cfg: "DictConfig"):  # noqa: F821
     device = torch.device(cfg.network.device)
 
+    # Create logger
     exp_name = generate_exp_name("DDPG", cfg.env.exp_name)
     logger = None
     if cfg.logger.backend:
@@ -43,40 +48,38 @@ def main(cfg: "DictConfig"):  # noqa: F821
             wandb_kwargs={"mode": cfg.logger.mode, "config": cfg},
         )
 
+    # Set seeds
     torch.manual_seed(cfg.env.seed)
     np.random.seed(cfg.env.seed)
 
-    # Create Environments
+    # Create environments
     train_env, eval_env = make_environment(cfg)
 
-    # Create Agent
+    # Create agent
     model, exploration_policy = make_ddpg_agent(cfg, train_env, eval_env, device)
 
-    # Create Loss Module and Target Updater
+    # Create DDPG loss
     loss_module, target_net_updater = make_loss_module(cfg, model)
 
-    # Make Off-Policy Collector
+    # Create off-policy collector
     collector = make_collector(cfg, train_env, exploration_policy)
 
-    # Make Replay Buffer
+    # Create replay buffer
     replay_buffer = make_replay_buffer(
         batch_size=cfg.optim.batch_size,
         prb=cfg.replay_buffer.prb,
         buffer_size=cfg.replay_buffer.size,
+        buffer_scratch_dir="/tmp/" + cfg.replay_buffer.scratch_dir,
         device=device,
     )
 
-    # Make Optimizers
+    # Create optimizers
     optimizer_actor, optimizer_critic = make_optimizer(cfg, loss_module)
 
-    rewards = []
-    rewards_eval = []
-
     # Main loop
+    start_time = time.time()
     collected_frames = 0
     pbar = tqdm.tqdm(total=cfg.collector.total_frames)
-    r0 = None
-    q_loss = None
 
     init_random_frames = cfg.collector.init_random_frames
     num_updates = int(
@@ -85,95 +88,109 @@ def main(cfg: "DictConfig"):  # noqa: F821
         * cfg.optim.utd_ratio
     )
     prb = cfg.replay_buffer.prb
-    env_per_collector = cfg.collector.env_per_collector
-    frames_per_batch, frame_skip = cfg.collector.frames_per_batch, cfg.env.frame_skip
+    frames_per_batch = cfg.collector.frames_per_batch
     eval_iter = cfg.logger.eval_iter
-    eval_rollout_steps = cfg.collector.max_frames_per_traj // frame_skip
+    eval_rollout_steps = cfg.env.max_episode_steps
 
-    for i, tensordict in enumerate(collector):
+    sampling_start = time.time()
+    for _, tensordict in enumerate(collector):
+        sampling_time = time.time() - sampling_start
+        # Update exploration policy
         exploration_policy.step(tensordict.numel())
-        # update weights of the inference policy
+
+        # Update weights of the inference policy
         collector.update_policy_weights_()
 
-        if r0 is None:
-            r0 = tensordict["next", "reward"].sum(-1).mean().item()
         pbar.update(tensordict.numel())
 
         tensordict = tensordict.reshape(-1)
         current_frames = tensordict.numel()
+        # Add to replay buffer
         replay_buffer.extend(tensordict.cpu())
         collected_frames += current_frames
 
-        # optimization steps
+        # Optimization steps
+        training_start = time.time()
         if collected_frames >= init_random_frames:
             (
                 actor_losses,
                 q_losses,
             ) = ([], [])
             for _ in range(num_updates):
-                # sample from replay buffer
+                # Sample from replay buffer
                 sampled_tensordict = replay_buffer.sample().clone()
 
+                # Compute loss
                 loss_td = loss_module(sampled_tensordict)
 
-                optimizer_critic.zero_grad()
-                optimizer_actor.zero_grad()
-
                 actor_loss = loss_td["loss_actor"]
                 q_loss = loss_td["loss_value"]
-                (actor_loss + q_loss).backward()
 
+                # Update critic
+                optimizer_critic.zero_grad()
+                q_loss.backward()
                 optimizer_critic.step()
-                q_losses.append(q_loss.item())
 
+                # Update actor
+                optimizer_actor.zero_grad()
+                actor_loss.backward()
                 optimizer_actor.step()
+
+                q_losses.append(q_loss.item())
                 actor_losses.append(actor_loss.item())
 
-                # update qnet_target params
+                # Update qnet_target params
                 target_net_updater.step()
 
-                # update priority
+                # Update priority
                 if prb:
                     replay_buffer.update_priority(sampled_tensordict)
 
-        rewards.append(
-            (i, tensordict["next", "reward"].sum().item() / env_per_collector)
+        training_time = time.time() - training_start
+        episode_end = (
+            tensordict["next", "done"]
+            if tensordict["next", "done"].any()
+            else tensordict["next", "truncated"]
         )
-        train_log = {
-            "train_reward": rewards[-1][1],
-            "collected_frames": collected_frames,
-        }
-        if q_loss is not None:
-            train_log.update(
-                {
-                    "actor_loss": np.mean(actor_losses),
-                    "q_loss": np.mean(q_losses),
-                }
+        episode_rewards = tensordict["next", "episode_reward"][episode_end]
+
+        # Logging
+        metrics_to_log = {}
+        if len(episode_rewards) > 0:
+            episode_length = tensordict["next", "step_count"][episode_end]
+            metrics_to_log["train/reward"] = episode_rewards.mean().item()
+            metrics_to_log["train/episode_length"] = episode_length.sum().item() / len(
+                episode_length
             )
-        if logger is not None:
-            for key, value in train_log.items():
-                logger.log_scalar(key, value, step=collected_frames)
-        if abs(collected_frames % eval_iter) < frames_per_batch * frame_skip:
+
+        if collected_frames >= init_random_frames:
+            metrics_to_log["train/q_loss"] = np.mean(q_losses)
+            metrics_to_log["train/a_loss"] = np.mean(actor_losses)
+            metrics_to_log["train/sampling_time"] = sampling_time
+            metrics_to_log["train/training_time"] = training_time
+
+        # Evaluation
+        if abs(collected_frames % eval_iter) < frames_per_batch:
             with set_exploration_type(ExplorationType.MODE), torch.no_grad():
+                eval_start = time.time()
                 eval_rollout = eval_env.rollout(
                     eval_rollout_steps,
                     exploration_policy,
                     auto_cast_to_device=True,
                     break_when_any_done=True,
                 )
+                eval_time = time.time() - eval_start
                 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})"
-                if logger is not None:
-                    logger.log_scalar(
-                        "evaluation_reward", rewards_eval[-1][1], step=collected_frames
-                    )
-        if len(rewards_eval):
-            pbar.set_description(
-                f"reward: {rewards[-1][1]: 4.4f} (r0 = {r0: 4.4f})," + eval_str
-            )
+                metrics_to_log["eval/reward"] = eval_reward
+                metrics_to_log["eval/time"] = eval_time
+        if logger is not None:
+            log_metrics(logger, metrics_to_log, collected_frames)
+        sampling_start = time.time()
 
     collector.shutdown()
+    end_time = time.time()
+    execution_time = end_time - start_time
+    print(f"Training took {execution_time:.2f} seconds to finish")
 
 
 if __name__ == "__main__":