Add offline trainer and discrete BCQ algorithm (#263)

The result needs to be tuned after `done` issue fixed.

Co-authored-by: n+e <trinkle23897@gmail.com>

Author: zhujl1991

.gitignore

@@ -145,3 +145,5 @@ MUJOCO_LOG.TXT
 *.zip
 *.pstats
 *.swp
+*.pkl
+*.hdf5

README.md

@@ -31,6 +31,7 @@
 - [Soft Actor-Critic (SAC)](https://arxiv.org/pdf/1812.05905.pdf)
 - [Discrete Soft Actor-Critic (SAC-Discrete)](https://arxiv.org/pdf/1910.07207.pdf)
 - Vanilla Imitation Learning
+- [Discrete Batch-Constrained deep Q-Learning (BCQ-Discrete)](https://arxiv.org/pdf/1910.01708.pdf)
 - [Prioritized Experience Replay (PER)](https://arxiv.org/pdf/1511.05952.pdf)
 - [Generalized Advantage Estimator (GAE)](https://arxiv.org/pdf/1506.02438.pdf)
 - [Posterior Sampling Reinforcement Learning (PSRL)](https://www.ece.uvic.ca/~bctill/papers/learning/Strens_2000.pdf)

docs/index.rst

@@ -20,8 +20,9 @@ Welcome to Tianshou!
 * :class:`~tianshou.policy.TD3Policy` `Twin Delayed DDPG <https://arxiv.org/pdf/1802.09477.pdf>`_
 * :class:`~tianshou.policy.SACPolicy` `Soft Actor-Critic <https://arxiv.org/pdf/1812.05905.pdf>`_
 * :class:`~tianshou.policy.DiscreteSACPolicy` `Discrete Soft Actor-Critic <https://arxiv.org/pdf/1910.07207.pdf>`_
-* :class:`~tianshou.policy.PSRLPolicy` `Posterior Sampling Reinforcement Learning <https://www.ece.uvic.ca/~bctill/papers/learning/Strens_2000.pdf>`_
 * :class:`~tianshou.policy.ImitationPolicy` Imitation Learning
+* :class:`~tianshou.policy.DiscreteBCQPolicy` `Discrete Batch-Constrained deep Q-Learning <https://arxiv.org/pdf/1910.01708.pdf>`_
+* :class:`~tianshou.policy.PSRLPolicy` `Posterior Sampling Reinforcement Learning <https://www.ece.uvic.ca/~bctill/papers/learning/Strens_2000.pdf>`_
 * :class:`~tianshou.data.PrioritizedReplayBuffer` `Prioritized Experience Replay <https://arxiv.org/pdf/1511.05952.pdf>`_
 * :meth:`~tianshou.policy.BasePolicy.compute_episodic_return` `Generalized Advantage Estimator <https://arxiv.org/pdf/1506.02438.pdf>`_
 

docs/tutorials/concepts.rst

@@ -201,7 +201,7 @@ Trainer
 
 Once you have a collector and a policy, you can start writing the training method for your RL agent. Trainer, to be honest, is a simple wrapper. It helps you save energy for writing the training loop. You can also construct your own trainer: :ref:`customized_trainer`.
 
-Tianshou has two types of trainer: :func:`~tianshou.trainer.onpolicy_trainer` and :func:`~tianshou.trainer.offpolicy_trainer`, corresponding to on-policy algorithms (such as Policy Gradient) and off-policy algorithms (such as DQN). Please check out :doc:`/api/tianshou.trainer` for the usage.
+Tianshou has three types of trainer: :func:`~tianshou.trainer.onpolicy_trainer` for on-policy algorithms such as Policy Gradient, :func:`~tianshou.trainer.offpolicy_trainer` for off-policy algorithms such as DQN, and :func:`~tianshou.trainer.offline_trainer` for offline algorithms such as BCQ. Please check out :doc:`/api/tianshou.trainer` for the usage.
 
 
 .. _pseudocode:

docs/tutorials/dqn.rst

@@ -120,7 +120,7 @@ In each step, the collector will let the policy perform (at least) a specified n
 Train Policy with a Trainer
 ---------------------------
 
-Tianshou provides :class:`~tianshou.trainer.onpolicy_trainer` and :class:`~tianshou.trainer.offpolicy_trainer`. The trainer will automatically stop training when the policy reach the stop condition ``stop_fn`` on test collector. Since DQN is an off-policy algorithm, we use the :class:`~tianshou.trainer.offpolicy_trainer` as follows:
+Tianshou provides :func:`~tianshou.trainer.onpolicy_trainer`, :func:`~tianshou.trainer.offpolicy_trainer`, and :func:`~tianshou.trainer.offline_trainer`. The trainer will automatically stop training when the policy reach the stop condition ``stop_fn`` on test collector. Since DQN is an off-policy algorithm, we use the :func:`~tianshou.trainer.offpolicy_trainer` as follows:
 ::
 
     result = ts.trainer.offpolicy_trainer(
@@ -133,7 +133,7 @@ Tianshou provides :class:`~tianshou.trainer.onpolicy_trainer` and :class:`~tians
         writer=None)
     print(f'Finished training! Use {result["duration"]}')
 
-The meaning of each parameter is as follows (full description can be found at :meth:`~tianshou.trainer.offpolicy_trainer`):
+The meaning of each parameter is as follows (full description can be found at :func:`~tianshou.trainer.offpolicy_trainer`):
 
 * ``max_epoch``: The maximum of epochs for training. The training process might be finished before reaching the ``max_epoch``;
 * ``step_per_epoch``: The number of step for updating policy network in one epoch;

examples/atari/README.md

@@ -38,4 +38,15 @@ One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.
 | SeaquestNoFrameskip-v4      | 6226        | ![](results/c51/Seaquest_rew.png)     | `python3 atari_c51.py --task "SeaquestNoFrameskip-v4"`  |
 | SpaceInvadersNoFrameskip-v4 | 988.5      | ![](results/c51/SpaceInvader_rew.png) | `python3 atari_c51.py --task "SpaceInvadersNoFrameskip-v4"`  |
 
-Note: The selection of `n_step` is based on Figure 6 in the [Rainbow](https://arxiv.org/abs/1710.02298) paper.
+Note: The selection of `n_step` is based on Figure 6 in the [Rainbow](https://arxiv.org/abs/1710.02298) paper.
+
+# BCQ
+
+TODO: after the `done` issue fixed, the result should be re-tuned and place here.
+
+To running BCQ algorithm on Atari, you need to do the following things:
+
+- Train an expert, by using the command listed in the above DQN section;
+- Generate buffer with noise: `python3 atari_dqn.py --task {your_task} --watch --resume-path log/{your_task}/dqn/policy.pth --eps-test 0.2 --buffer-size 1000000 --save-buffer-name expert.hdf5` (note that 1M Atari buffer cannot be saved as `.pkl` format because it is too large and will cause error);
+- Train BCQ: `python3 atari_bcq.py --task {your_task} --load-buffer-name expert.hdf5`.
+

examples/atari/atari_bcq.py

@@ -0,0 +1,153 @@
+import os
+import torch
+import pickle
+import pprint
+import argparse
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+
+from tianshou.env import SubprocVectorEnv
+from tianshou.trainer import offline_trainer
+from tianshou.utils.net.discrete import Actor
+from tianshou.policy import DiscreteBCQPolicy
+from tianshou.data import Collector, ReplayBuffer
+
+from atari_network import DQN
+from atari_wrapper import wrap_deepmind
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--task", type=str, default="PongNoFrameskip-v4")
+    parser.add_argument("--seed", type=int, default=1626)
+    parser.add_argument("--eps-test", type=float, default=0.001)
+    parser.add_argument("--lr", type=float, default=6.25e-5)
+    parser.add_argument("--gamma", type=float, default=0.99)
+    parser.add_argument("--n-step", type=int, default=3)
+    parser.add_argument("--target-update-freq", type=int, default=8000)
+    parser.add_argument("--unlikely-action-threshold", type=float, default=0.3)
+    parser.add_argument("--imitation-logits-penalty", type=float, default=0.01)
+    parser.add_argument("--epoch", type=int, default=100)
+    parser.add_argument("--step-per-epoch", type=int, default=10000)
+    parser.add_argument("--batch-size", type=int, default=32)
+    parser.add_argument('--hidden-sizes', type=int,
+                        nargs='*', default=[512])
+    parser.add_argument("--test-num", type=int, default=100)
+    parser.add_argument('--frames_stack', type=int, default=4)
+    parser.add_argument("--logdir", type=str, default="log")
+    parser.add_argument("--render", type=float, default=0.)
+    parser.add_argument("--resume-path", type=str, default=None)
+    parser.add_argument("--watch", default=False, action="store_true",
+                        help="watch the play of pre-trained policy only")
+    parser.add_argument("--log-interval", type=int, default=1000)
+    parser.add_argument(
+        "--load-buffer-name", type=str,
+        default="./expert_DQN_PongNoFrameskip-v4.hdf5",
+    )
+    parser.add_argument(
+        "--device", type=str,
+        default="cuda" if torch.cuda.is_available() else "cpu",
+    )
+    args = parser.parse_known_args()[0]
+    return args
+
+
+def make_atari_env(args):
+    return wrap_deepmind(args.task, frame_stack=args.frames_stack)
+
+
+def make_atari_env_watch(args):
+    return wrap_deepmind(args.task, frame_stack=args.frames_stack,
+                         episode_life=False, clip_rewards=False)
+
+
+def test_discrete_bcq(args=get_args()):
+    # envs
+    env = make_atari_env(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
+    # should be N_FRAMES x H x W
+    print("Observations shape:", args.state_shape)
+    print("Actions shape:", args.action_shape)
+    # make environments
+    test_envs = SubprocVectorEnv([lambda: make_atari_env_watch(args)
+                                  for _ in range(args.test_num)])
+    # seed
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    test_envs.seed(args.seed)
+    # model
+    feature_net = DQN(*args.state_shape, args.action_shape,
+                      device=args.device, features_only=True).to(args.device)
+    policy_net = Actor(feature_net, args.action_shape,
+                       hidden_sizes=args.hidden_sizes).to(args.device)
+    imitation_net = Actor(feature_net, args.action_shape,
+                          hidden_sizes=args.hidden_sizes).to(args.device)
+    optim = torch.optim.Adam(
+        set(policy_net.parameters()).union(imitation_net.parameters()),
+        lr=args.lr,
+    )
+    # define policy
+    policy = DiscreteBCQPolicy(
+        policy_net, imitation_net, optim, args.gamma, args.n_step,
+        args.target_update_freq, args.eps_test,
+        args.unlikely_action_threshold, args.imitation_logits_penalty,
+    )
+    # 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)
+    # buffer
+    assert os.path.exists(args.load_buffer_name), \
+        "Please run atari_dqn.py first to get expert's data buffer."
+    if args.load_buffer_name.endswith('.pkl'):
+        buffer = pickle.load(open(args.load_buffer_name, "rb"))
+    elif args.load_buffer_name.endswith('.hdf5'):
+        buffer = ReplayBuffer.load_hdf5(args.load_buffer_name)
+    else:
+        print(f"Unknown buffer format: {args.load_buffer_name}")
+        exit(0)
+
+    # collector
+    test_collector = Collector(policy, test_envs)
+
+    log_path = os.path.join(args.logdir, args.task, 'discrete_bcq')
+    writer = SummaryWriter(log_path)
+
+    def save_fn(policy):
+        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
+
+    def stop_fn(mean_rewards):
+        return False
+
+    # watch agent's performance
+    def watch():
+        print("Setup test envs ...")
+        policy.eval()
+        policy.set_eps(args.eps_test)
+        test_envs.seed(args.seed)
+        print("Testing agent ...")
+        test_collector.reset()
+        result = test_collector.collect(n_episode=[1] * args.test_num,
+                                        render=args.render)
+        pprint.pprint(result)
+
+    if args.watch:
+        watch()
+        exit(0)
+
+    result = offline_trainer(
+        policy, buffer, test_collector,
+        args.epoch, args.step_per_epoch, args.test_num, args.batch_size,
+        stop_fn=stop_fn, save_fn=save_fn, writer=writer,
+        log_interval=args.log_interval,
+    )
+
+    pprint.pprint(result)
+    watch()
+
+
+if __name__ == "__main__":
+    test_discrete_bcq(get_args())

examples/atari/atari_dqn.py

@@ -41,6 +41,7 @@ def get_args():
     parser.add_argument('--resume-path', type=str, default=None)
     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)
     return parser.parse_args()
 
 
@@ -120,13 +121,25 @@ def test_fn(epoch, env_step):
 
     # watch agent's performance
     def watch():
-        print("Testing agent ...")
+        print("Setup test envs ...")
         policy.eval()
         policy.set_eps(args.eps_test)
         test_envs.seed(args.seed)
-        test_collector.reset()
-        result = test_collector.collect(n_episode=[1] * args.test_num,
-                                        render=args.render)
+        if args.save_buffer_name:
+            print(f"Generate buffer with size {args.buffer_size}")
+            buffer = ReplayBuffer(
+                args.buffer_size, ignore_obs_next=True,
+                save_only_last_obs=True, stack_num=args.frames_stack)
+            collector = Collector(policy, test_envs, buffer)
+            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=[1] * args.test_num,
+                                            render=args.render)
         pprint.pprint(result)
 
     if args.watch:

examples/atari/atari_network.py

@@ -44,8 +44,7 @@ def forward(
         info: Dict[str, Any] = {},
     ) -> Tuple[torch.Tensor, Any]:
         r"""Mapping: x -> Q(x, \*)."""
-        x = torch.as_tensor(
-            x, device=self.device, dtype=torch.float32)  # type: ignore
+        x = torch.as_tensor(x, device=self.device, dtype=torch.float32)
         return self.net(x), state
 
 

setup.py

@@ -51,7 +51,7 @@ def get_version() -> str:
         "tensorboard",
         "torch>=1.4.0",
         "numba>=0.51.0",
-        "h5py>=3.1.0"
+        "h5py>=2.10.0",  # to match tensorflow's minimal requirements
     ],
     extras_require={
         "dev": [