[Algorithm] Discrete CQL

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

@@ -106,6 +106,16 @@ python .github/unittest/helpers/coverage_run_parallel.py examples/dqn/dqn.py \
  record_video=True \
  record_frames=4 \
  buffer_size=120
+python .github/unittest/helpers/coverage_run_parallel.py examples/cql/discrete_cql_online.py \
+ collector.total_frames=48 \
+ collector.init_random_frames=10 \
+ optim.batch_size=10 \
+ collector.frames_per_batch=16 \
+ collector.env_per_collector=2 \
+ collector.device=cuda:0 \
+ optim.optim_steps_per_batch=1 \
+ replay_buffer.size=120 \
+ logger.backend=
 python .github/unittest/helpers/coverage_run_parallel.py examples/redq/redq.py \
  num_workers=4 \
  collector.total_frames=48 \

docs/source/reference/objectives.rst

@@ -136,6 +136,7 @@ CQL
  :template: rl_template_noinherit.rst
 
  CQLLoss
+ DiscreteCQLLoss
 
 DT
 ----

examples/cql/discrete_cql_config.yaml

@@ -0,0 +1,57 @@
+# Task and env
+env:
+ name: CartPole-v1
+ task: ""
+ library: gym
+ exp_name: cql_cartpole_gym
+ n_samples_stats: 1000
+ max_episode_steps: 200
+ seed: 0
+
+# Collector
+collector:
+ frames_per_batch: 200
+ total_frames: 20000 
+ multi_step: 0
+ init_random_frames: 1000
+ env_per_collector: 1
+ device: cpu
+ max_frames_per_traj: 200
+ annealing_frames: 10000
+ eps_start: 1.0
+ eps_end: 0.01
+# logger
+logger:
+ backend: wandb
+ log_interval: 5000 # record interval in frames
+ eval_steps: 200
+ mode: online
+ eval_iter: 1000
+
+# Buffer
+replay_buffer:
+ prb: 0
+ buffer_prefetch: 64
+ size: 1_000_000
+ scratch_dir: ${env.exp_name}_${env.seed}
+
+# Optimization
+optim:
+ utd_ratio: 1
+ device: cuda:0
+ lr: 1e-3
+ weight_decay: 0.0
+ batch_size: 256
+ lr_scheduler: ""
+ optim_steps_per_batch: 200
+
+# Policy and model
+model:
+ hidden_sizes: [256, 256]
+ activation: relu
+
+# loss
+loss: 
+ loss_function: l2
+ gamma: 0.99
+ tau: 0.005

examples/cql/discrete_cql_online.py

@@ -0,0 +1,199 @@
+# 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.
+"""Discrete (DQN) CQL Example.
+
+This is a simple self-contained example of a discrete CQL training script.
+
+It supports state environments like gym and gymnasium.
+
+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_cql_optimizer,
+ make_discretecql_model,
+ make_discreteloss,
+ make_environment,
+ make_replay_buffer,
+)
+
+
+@hydra.main(version_base="1.1", config_path=".", config_name="discrete_cql_config")
+def main(cfg: "DictConfig"): # noqa: F821
+ device = torch.device(cfg.optim.device)
+
+ # Create logger
+ exp_name = generate_exp_name("DiscreteCQL", cfg.env.exp_name)
+ logger = None
+ if cfg.logger.backend:
+ logger = get_logger(
+ logger_type=cfg.logger.backend,
+ logger_name="discretecql_logging",
+ experiment_name=exp_name,
+ wandb_kwargs={"mode": cfg.logger.mode, "config": cfg},
+ )
+
+ # Set seeds
+ torch.manual_seed(cfg.env.seed)
+ np.random.seed(cfg.env.seed)
+
+ # Create environments
+ train_env, eval_env = make_environment(cfg)
+
+ # Create agent
+ model, explore_policy = make_discretecql_model(cfg, train_env, eval_env, device)
+
+ # Create loss
+ loss_module, target_net_updater = make_discreteloss(cfg.loss, model)
+
+ # Create off-policy collector
+ collector = make_collector(cfg, train_env, explore_policy)
+
+ # 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=cfg.replay_buffer.scratch_dir,
+ device="cpu",
+ )
+
+ # Create optimizers
+ optimizer = make_cql_optimizer(cfg, loss_module)
+
+ # Main loop
+ 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_rollout_steps = cfg.env.max_episode_steps
+ eval_iter = cfg.logger.eval_iter
+ frames_per_batch = cfg.collector.frames_per_batch
+
+ start_time = sampling_start = time.time()
+ for tensordict in collector:
+ sampling_time = time.time() - sampling_start
+
+ # Update exploration policy
+ explore_policy[1].step(tensordict.numel())
+
+ # 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:
+ (
+ q_losses,
+ cql_losses,
+ ) = ([], [])
+ for _ in range(num_updates):
+
+ # 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
+ loss_dict = loss_module(sampled_tensordict)
+
+ q_loss = loss_dict["loss_qvalue"]
+ cql_loss = loss_dict["loss_cql"]
+ loss = q_loss + cql_loss
+
+ # Update model
+ optimizer.zero_grad()
+ loss.backward()
+ optimizer.step()
+ q_losses.append(q_loss.item())
+ cql_losses.append(cql_loss.item())
+
+ # Update target params
+ target_net_updater.step()
+ # 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
+ )
+ metrics_to_log["train/epsilon"] = explore_policy[1].eps
+
+ if collected_frames >= init_random_frames:
+ metrics_to_log["train/q_loss"] = np.mean(q_losses)
+ metrics_to_log["train/cql_loss"] = np.mean(cql_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,
+ model,
+ 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
+ print(f"Training took {execution_time:.2f} seconds to finish")
+
+
+if __name__ == "__main__":
+ main()