Add Atari SAC examples (#657)

- Add Atari (discrete) SAC examples;
- Fix a bug in Discrete SAC evaluation; default to deterministic mode.

examples/atari/README.md

@@ -121,3 +121,17 @@ One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.
 | MsPacmanNoFrameskip-v4      | 1930       | ![](results/ppo/MsPacman_rew.png)     | `python3 atari_ppo.py --task "MsPacmanNoFrameskip-v4"`  |
 | SeaquestNoFrameskip-v4      | 904      | ![](results/ppo/Seaquest_rew.png)     | `python3 atari_ppo.py --task "SeaquestNoFrameskip-v4" --lr 2.5e-5`  |
 | SpaceInvadersNoFrameskip-v4 | 843       | ![](results/ppo/SpaceInvaders_rew.png) | `python3 atari_ppo.py --task "SpaceInvadersNoFrameskip-v4"`  |
+
+# SAC (single run)
+
+One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.
+
+| task                        | best reward | reward curve                          | parameters                                                   |
+| --------------------------- | ----------- | ------------------------------------- | ------------------------------------------------------------ |
+| PongNoFrameskip-v4          | 20.1        | ![](results/discrete_sac/Pong_rew.png)         | `python3 atari_sac.py --task "PongNoFrameskip-v4"` |
+| BreakoutNoFrameskip-v4      | 211.2        | ![](results/discrete_sac/Breakout_rew.png)     | `python3 atari_sac.py --task "BreakoutNoFrameskip-v4" --n-step 1 --actor-lr 1e-4 --critic-lr 1e-4` |
+| EnduroNoFrameskip-v4        | 1290.7       | ![](results/discrete_sac/Enduro_rew.png)       | `python3 atari_sac.py --task "EnduroNoFrameskip-v4"`  |
+| QbertNoFrameskip-v4         | 13157.5      | ![](results/discrete_sac/Qbert_rew.png)        | `python3 atari_sac.py --task "QbertNoFrameskip-v4"`  |
+| MsPacmanNoFrameskip-v4      | 3836       | ![](results/discrete_sac/MsPacman_rew.png)     | `python3 atari_sac.py --task "MsPacmanNoFrameskip-v4"`  |
+| SeaquestNoFrameskip-v4      | 1772      | ![](results/discrete_sac/Seaquest_rew.png)     | `python3 atari_sac.py --task "SeaquestNoFrameskip-v4"`  |
+| SpaceInvadersNoFrameskip-v4 | 649       | ![](results/discrete_sac/SpaceInvaders_rew.png) | `python3 atari_sac.py --task "SpaceInvadersNoFrameskip-v4"`  |

examples/atari/atari_ppo.py

@@ -162,7 +162,7 @@ def test_ppo(args=get_args()):
             feature_net.net,
             feature_dim,
             action_dim,
-            hidden_sizes=args.hidden_sizes,
+            hidden_sizes=[args.hidden_size],
             device=args.device,
         )
         icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.lr)

examples/atari/atari_sac.py

@@ -0,0 +1,269 @@
+import argparse
+import datetime
+import os
+import pprint
+
+import numpy as np
+import torch
+from atari_network import DQN
+from atari_wrapper import make_atari_env
+from torch.utils.tensorboard import SummaryWriter
+
+from tianshou.data import Collector, VectorReplayBuffer
+from tianshou.policy import DiscreteSACPolicy, ICMPolicy
+from tianshou.trainer import offpolicy_trainer
+from tianshou.utils import TensorboardLogger, WandbLogger
+from tianshou.utils.net.discrete import Actor, Critic, IntrinsicCuriosityModule
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--task", type=str, default="PongNoFrameskip-v4")
+    parser.add_argument("--seed", type=int, default=4213)
+    parser.add_argument("--scale-obs", type=int, default=0)
+    parser.add_argument("--buffer-size", type=int, default=100000)
+    parser.add_argument("--actor-lr", type=float, default=1e-5)
+    parser.add_argument("--critic-lr", type=float, default=1e-5)
+    parser.add_argument("--gamma", type=float, default=0.99)
+    parser.add_argument("--n-step", type=int, default=3)
+    parser.add_argument("--tau", type=float, default=0.005)
+    parser.add_argument("--alpha", type=float, default=0.05)
+    parser.add_argument("--auto-alpha", action="store_true", default=False)
+    parser.add_argument("--alpha-lr", type=float, default=3e-4)
+    parser.add_argument("--epoch", type=int, default=100)
+    parser.add_argument("--step-per-epoch", type=int, default=100000)
+    parser.add_argument("--step-per-collect", type=int, default=10)
+    parser.add_argument("--update-per-step", type=float, default=0.1)
+    parser.add_argument("--batch-size", type=int, default=64)
+    parser.add_argument("--hidden-size", type=int, default=512)
+    parser.add_argument("--training-num", type=int, default=10)
+    parser.add_argument("--test-num", type=int, default=10)
+    parser.add_argument("--rew-norm", type=int, default=False)
+    parser.add_argument("--logdir", type=str, default="log")
+    parser.add_argument("--render", type=float, default=0.)
+    parser.add_argument(
+        "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu"
+    )
+    parser.add_argument("--frames-stack", type=int, default=4)
+    parser.add_argument("--resume-path", type=str, default=None)
+    parser.add_argument("--resume-id", type=str, default=None)
+    parser.add_argument(
+        "--logger",
+        type=str,
+        default="tensorboard",
+        choices=["tensorboard", "wandb"],
+    )
+    parser.add_argument("--wandb-project", type=str, default="atari.benchmark")
+    parser.add_argument(
+        "--watch",
+        default=False,
+        action="store_true",
+        help="watch the play of pre-trained policy only"
+    )
+    parser.add_argument("--save-buffer-name", type=str, default=None)
+    parser.add_argument(
+        "--icm-lr-scale",
+        type=float,
+        default=0.,
+        help="use intrinsic curiosity module with this lr scale"
+    )
+    parser.add_argument(
+        "--icm-reward-scale",
+        type=float,
+        default=0.01,
+        help="scaling factor for intrinsic curiosity reward"
+    )
+    parser.add_argument(
+        "--icm-forward-loss-weight",
+        type=float,
+        default=0.2,
+        help="weight for the forward model loss in ICM"
+    )
+    return parser.parse_args()
+
+
+def test_discrete_sac(args=get_args()):
+    env, train_envs, test_envs = make_atari_env(
+        args.task,
+        args.seed,
+        args.training_num,
+        args.test_num,
+        scale=args.scale_obs,
+        frame_stack=args.frames_stack,
+    )
+    args.state_shape = env.observation_space.shape or env.observation_space.n
+    args.action_shape = env.action_space.shape or env.action_space.n
+    # should be N_FRAMES x H x W
+    print("Observations shape:", args.state_shape)
+    print("Actions shape:", args.action_shape)
+    # seed
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    # define model
+    net = DQN(
+        *args.state_shape,
+        args.action_shape,
+        device=args.device,
+        features_only=True,
+        output_dim=args.hidden_size
+    )
+    actor = Actor(net, args.action_shape, device=args.device, softmax_output=False)
+    actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
+    critic1 = Critic(net, last_size=args.action_shape, device=args.device)
+    critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
+    critic2 = Critic(net, last_size=args.action_shape, device=args.device)
+    critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
+
+    # define policy
+    if args.auto_alpha:
+        target_entropy = 0.98 * np.log(np.prod(args.action_shape))
+        log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
+        alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
+        args.alpha = (target_entropy, log_alpha, alpha_optim)
+
+    policy = DiscreteSACPolicy(
+        actor,
+        actor_optim,
+        critic1,
+        critic1_optim,
+        critic2,
+        critic2_optim,
+        args.tau,
+        args.gamma,
+        args.alpha,
+        estimation_step=args.n_step,
+        reward_normalization=args.rew_norm,
+    ).to(args.device)
+    if args.icm_lr_scale > 0:
+        feature_net = DQN(
+            *args.state_shape, args.action_shape, args.device, features_only=True
+        )
+        action_dim = np.prod(args.action_shape)
+        feature_dim = feature_net.output_dim
+        icm_net = IntrinsicCuriosityModule(
+            feature_net.net,
+            feature_dim,
+            action_dim,
+            hidden_sizes=[args.hidden_size],
+            device=args.device,
+        )
+        icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.actor_lr)
+        policy = ICMPolicy(
+            policy, icm_net, icm_optim, args.icm_lr_scale, args.icm_reward_scale,
+            args.icm_forward_loss_weight
+        ).to(args.device)
+    # load a previous policy
+    if args.resume_path:
+        policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
+        print("Loaded agent from: ", args.resume_path)
+    # replay buffer: `save_last_obs` and `stack_num` can be removed together
+    # when you have enough RAM
+    buffer = VectorReplayBuffer(
+        args.buffer_size,
+        buffer_num=len(train_envs),
+        ignore_obs_next=True,
+        save_only_last_obs=True,
+        stack_num=args.frames_stack,
+    )
+    # collector
+    train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
+    test_collector = Collector(policy, test_envs, exploration_noise=True)
+
+    # log
+    now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
+    args.algo_name = "discrete_sac_icm" if args.icm_lr_scale > 0 else "discrete_sac"
+    log_name = os.path.join(args.task, args.algo_name, str(args.seed), now)
+    log_path = os.path.join(args.logdir, log_name)
+
+    # logger
+    if args.logger == "wandb":
+        logger = WandbLogger(
+            save_interval=1,
+            name=log_name.replace(os.path.sep, "__"),
+            run_id=args.resume_id,
+            config=args,
+            project=args.wandb_project,
+        )
+    writer = SummaryWriter(log_path)
+    writer.add_text("args", str(args))
+    if args.logger == "tensorboard":
+        logger = TensorboardLogger(writer)
+    else:  # wandb
+        logger.load(writer)
+
+    def save_best_fn(policy):
+        torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
+
+    def stop_fn(mean_rewards):
+        if env.spec.reward_threshold:
+            return mean_rewards >= env.spec.reward_threshold
+        elif "Pong" in args.task:
+            return mean_rewards >= 20
+        else:
+            return False
+
+    def save_checkpoint_fn(epoch, env_step, gradient_step):
+        # see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
+        ckpt_path = os.path.join(log_path, "checkpoint.pth")
+        torch.save({"model": policy.state_dict()}, ckpt_path)
+        return ckpt_path
+
+    # watch agent's performance
+    def watch():
+        print("Setup test envs ...")
+        policy.eval()
+        test_envs.seed(args.seed)
+        if args.save_buffer_name:
+            print(f"Generate buffer with size {args.buffer_size}")
+            buffer = VectorReplayBuffer(
+                args.buffer_size,
+                buffer_num=len(test_envs),
+                ignore_obs_next=True,
+                save_only_last_obs=True,
+                stack_num=args.frames_stack,
+            )
+            collector = Collector(policy, test_envs, buffer, exploration_noise=True)
+            result = collector.collect(n_step=args.buffer_size)
+            print(f"Save buffer into {args.save_buffer_name}")
+            # Unfortunately, pickle will cause oom with 1M buffer size
+            buffer.save_hdf5(args.save_buffer_name)
+        else:
+            print("Testing agent ...")
+            test_collector.reset()
+            result = test_collector.collect(
+                n_episode=args.test_num, render=args.render
+            )
+        rew = result["rews"].mean()
+        print(f"Mean reward (over {result['n/ep']} episodes): {rew}")
+
+    if args.watch:
+        watch()
+        exit(0)
+
+    # test train_collector and start filling replay buffer
+    train_collector.collect(n_step=args.batch_size * args.training_num)
+    # trainer
+    result = offpolicy_trainer(
+        policy,
+        train_collector,
+        test_collector,
+        args.epoch,
+        args.step_per_epoch,
+        args.step_per_collect,
+        args.test_num,
+        args.batch_size,
+        stop_fn=stop_fn,
+        save_best_fn=save_best_fn,
+        logger=logger,
+        update_per_step=args.update_per_step,
+        test_in_train=False,
+        resume_from_log=args.resume_id is not None,
+        save_checkpoint_fn=save_checkpoint_fn,
+    )
+
+    pprint.pprint(result)
+    watch()
+
+
+if __name__ == "__main__":
+    test_discrete_sac(get_args())

test/discrete/test_sac.py

@@ -53,7 +53,7 @@ def test_discrete_sac(args=get_args()):
     args.state_shape = env.observation_space.shape or env.observation_space.n
     args.action_shape = env.action_space.shape or env.action_space.n
     if args.reward_threshold is None:
-        default_reward_threshold = {"CartPole-v0": 180}  # lower the goal
+        default_reward_threshold = {"CartPole-v0": 170}  # lower the goal
         args.reward_threshold = default_reward_threshold.get(
             args.task, env.spec.reward_threshold
         )

tianshou/policy/modelfree/discrete_sac.py

@@ -80,7 +80,10 @@ class DiscreteSACPolicy(SACPolicy):
         obs = batch[input]
         logits, hidden = self.actor(obs, state=state, info=batch.info)
         dist = Categorical(logits=logits)
-        act = dist.sample()
+        if self._deterministic_eval and not self.training:
+            act = logits.argmax(axis=-1)
+        else:
+            act = dist.sample()
         return Batch(logits=logits, act=act, state=hidden, dist=dist)
 
     def _target_q(self, buffer: ReplayBuffer, indices: np.ndarray) -> torch.Tensor: