[Algorithm] CrossQ

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

@@ -149,6 +149,20 @@ python .github/unittest/helpers/coverage_run_parallel.py sota-implementations/di
   replay_buffer.size=120 \
   env.name=CartPole-v1 \
   logger.backend=
+#  logger.record_video=True \
+#  logger.record_frames=4 \
+python .github/unittest/helpers/coverage_run_parallel.py sota-implementations/crossq/crossq.py \
+  collector.total_frames=48 \
+  collector.init_random_frames=10 \
+  collector.frames_per_batch=16 \
+  collector.env_per_collector=2 \
+  collector.device= \
+  optim.batch_size=10 \
+  optim.utd_ratio=1 \
+  replay_buffer.size=120 \
+  env.name=Pendulum-v1 \
+  network.device= \
+  logger.backend=
 python .github/unittest/helpers/coverage_run_parallel.py sota-implementations/dreamer/dreamer.py \
   collector.total_frames=200 \
   collector.init_random_frames=10 \

sota-implementations/crossq/config.yaml

@@ -0,0 +1,57 @@
+# environment and task
+env:
+  name: HalfCheetah-v4
+  task: ""
+  library: gym
+  max_episode_steps: 1000
+  seed: 42
+
+# collector
+collector:
+  total_frames: 1_000_000
+  init_random_frames: 25000
+  frames_per_batch: 1000
+  init_env_steps: 1000
+  device: cpu
+  env_per_collector: 1
+  reset_at_each_iter: False
+
+# replay buffer
+replay_buffer:
+  size: 1000000
+  prb: 0 # use prioritized experience replay
+  scratch_dir: null
+
+# optim
+optim:
+  utd_ratio: 1.0
+  policy_update_delay: 3
+  gamma: 0.99
+  loss_function: l2
+  lr: 3.0e-4
+  weight_decay: 0.0
+  batch_size: 256
+  alpha_init: 1.0
+  # Adam β1 = 0.5
+  adam_eps: 1.0e-8
+
+# network
+network:
+  batch_norm_momentum: 0.01
+  # warmup_steps: 100000 # 10^5
+  critic_hidden_sizes: [2048, 2048]
+  actor_hidden_sizes: [256, 256]
+  critic_activation: tanh
+  actor_activation: relu
+  default_policy_scale: 1.0
+  scale_lb: 0.1
+  device: "cuda:0"
+
+# logging
+logger:
+  backend: wandb
+  project_name: torchrl_example_crossQ
+  group_name: null
+  exp_name: ${env.name}_CrossQ
+  mode: online
+  eval_iter: 25000

sota-implementations/crossq/crossq.py

@@ -0,0 +1,225 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""CrossQ Example.
+
+This is a simple self-contained example of a CrossQ training script.
+
+It supports state environments like MuJoCo.
+
+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._utils import logger as torchrl_logger
+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_crossQ_agent,
+    make_crossQ_optimizer,
+    make_environment,
+    make_loss_module,
+    make_replay_buffer,
+)
+
+
+@hydra.main(version_base="1.1", config_path=".", config_name="config")
+def main(cfg: "DictConfig"):  # noqa: F821
+    device = torch.device(cfg.network.device)
+
+    # Create logger
+    exp_name = generate_exp_name("CrossQ", cfg.logger.exp_name)
+    logger = None
+    if cfg.logger.backend:
+        logger = get_logger(
+            logger_type=cfg.logger.backend,
+            logger_name="crossq_logging",
+            experiment_name=exp_name,
+            wandb_kwargs={
+                "mode": cfg.logger.mode,
+                "config": dict(cfg),
+                "project": cfg.logger.project_name,
+                "group": cfg.logger.group_name,
+            },
+        )
+
+    torch.manual_seed(cfg.env.seed)
+    np.random.seed(cfg.env.seed)
+
+    # Create environments
+    train_env, eval_env = make_environment(cfg)
+
+    # Create agent
+    model, exploration_policy = make_crossQ_agent(cfg, train_env, eval_env, device)
+
+    # Create CrossQ loss
+    loss_module = make_loss_module(cfg, model)
+
+    # Create off-policy collector
+    collector = make_collector(cfg, train_env, exploration_policy.eval())
+
+    # 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,
+        scratch_dir=cfg.replay_buffer.scratch_dir,
+        device="cpu",
+    )
+
+    # Create optimizers
+    (
+        optimizer_actor,
+        optimizer_critic,
+        optimizer_alpha,
+    ) = make_crossQ_optimizer(cfg, loss_module)
+
+    # Main loop
+    start_time = time.time()
+    collected_frames = 0
+    pbar = tqdm.tqdm(total=cfg.collector.total_frames)
+
+    init_random_frames = cfg.collector.init_random_frames
+    num_updates = int(
+        cfg.collector.env_per_collector
+        * cfg.collector.frames_per_batch
+        * cfg.optim.utd_ratio
+    )
+    prb = cfg.replay_buffer.prb
+    eval_iter = cfg.logger.eval_iter
+    frames_per_batch = cfg.collector.frames_per_batch
+    eval_rollout_steps = cfg.env.max_episode_steps
+
+    sampling_start = time.time()
+    update_counter = 0
+    delayed_updates = cfg.optim.policy_update_delay
+    for _, tensordict in enumerate(collector):
+        sampling_time = time.time() - sampling_start
+
+        # Update weights of the inference policy
+        collector.update_policy_weights_()
+
+        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
+        training_start = time.time()
+        if collected_frames >= init_random_frames:
+            (
+                actor_losses,
+                alpha_losses,
+                q_losses,
+            ) = ([], [], [])
+            for _ in range(num_updates):
+
+                # Update actor every delayed_updates
+                update_counter += 1
+                update_actor = update_counter % delayed_updates == 0
+                # Sample from replay buffer
+                sampled_tensordict = replay_buffer.sample()
+                if sampled_tensordict.device != device:
+                    sampled_tensordict = sampled_tensordict.to(
+                        device, non_blocking=True
+                    )
+                else:
+                    sampled_tensordict = sampled_tensordict.clone()
+
+                # Compute loss
+                q_loss, *_ = loss_module._qvalue_loss(sampled_tensordict)
+                q_loss = q_loss.mean()
+                # Update critic
+                optimizer_critic.zero_grad()
+                q_loss.backward()
+                optimizer_critic.step()
+                q_losses.append(q_loss.detach().item())
+
+                if update_actor:
+                    actor_loss, metadata_actor = loss_module._actor_loss(
+                        sampled_tensordict
+                    )
+                    actor_loss = actor_loss.mean()
+                    alpha_loss = loss_module._alpha_loss(
+                        log_prob=metadata_actor["log_prob"]
+                    )
+                    alpha_loss = alpha_loss.mean()
+                    # Update actor
+                    optimizer_actor.zero_grad()
+                    actor_loss.backward()
+                    optimizer_actor.step()
+
+                    # Update alpha
+                    optimizer_alpha.zero_grad()
+                    alpha_loss.backward()
+                    optimizer_alpha.step()
+
+                    actor_losses.append(actor_loss.detach().item())
+                    alpha_losses.append(alpha_loss.detach().item())
+
+                # Update priority
+                if prb:
+                    replay_buffer.update_priority(sampled_tensordict)
+
+        training_time = time.time() - training_start
+        episode_end = (
+            tensordict["next", "done"]
+            if tensordict["next", "done"].any()
+            else tensordict["next", "truncated"]
+        )
+        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 collected_frames >= init_random_frames:
+            metrics_to_log["train/q_loss"] = np.mean(q_losses).item()
+            metrics_to_log["train/actor_loss"] = np.mean(actor_losses).item()
+            metrics_to_log["train/alpha_loss"] = np.mean(alpha_losses).item()
+            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,
+                    model[0],
+                    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()
+                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
+    torchrl_logger.info(f"Training took {execution_time:.2f} seconds to finish")
+
+
+if __name__ == "__main__":
+    main()