thu-ml · Trinkle23897 · Jan 28, 2021 · Jan 19, 2021 · Jan 19, 2021 · Jan 19, 2021
diff --git a/README.md b/README.md
@@ -24,6 +24,7 @@
 - [Double DQN](https://arxiv.org/pdf/1509.06461.pdf)
 - [Dueling DQN](https://arxiv.org/pdf/1511.06581.pdf)
 - [C51](https://arxiv.org/pdf/1707.06887.pdf)
+- [Quantile Regression DQN (QRDQN)](https://arxiv.org/pdf/1710.10044.pdf)
 - [Advantage Actor-Critic (A2C)](https://openai.com/blog/baselines-acktr-a2c/)
 - [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/pdf/1509.02971.pdf)
 - [Proximal Policy Optimization (PPO)](https://arxiv.org/pdf/1707.06347.pdf)

diff --git a/docs/index.rst b/docs/index.rst
@@ -14,6 +14,7 @@ Welcome to Tianshou!
 * :class:`~tianshou.policy.DQNPolicy` `Double DQN <https://arxiv.org/pdf/1509.06461.pdf>`_
 * :class:`~tianshou.policy.DQNPolicy` `Dueling DQN <https://arxiv.org/pdf/1511.06581.pdf>`_
 * :class:`~tianshou.policy.C51Policy` `C51 <https://arxiv.org/pdf/1707.06887.pdf>`_
+* :class:`~tianshou.policy.QRDQNPolicy` `Quantile Regression DQN <https://arxiv.org/pdf/1710.10044.pdf>`_
 * :class:`~tianshou.policy.A2CPolicy` `Advantage Actor-Critic <https://openai.com/blog/baselines-acktr-a2c/>`_
 * :class:`~tianshou.policy.DDPGPolicy` `Deep Deterministic Policy Gradient <https://arxiv.org/pdf/1509.02971.pdf>`_
 * :class:`~tianshou.policy.PPOPolicy` `Proximal Policy Optimization <https://arxiv.org/pdf/1707.06347.pdf>`_

diff --git a/examples/atari/README.md b/examples/atari/README.md
@@ -40,6 +40,20 @@ One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.
 
 Note: The selection of `n_step` is based on Figure 6 in the [Rainbow](https://arxiv.org/abs/1710.02298) paper.
 
+# QRDQN (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          | ![](results/qrdqn/Pong_rew.png)         | `python3 atari_qrdqn.py --task "PongNoFrameskip-v4" --batch-size 64` |
+| BreakoutNoFrameskip-v4      | 409.2         | ![](results/qrdqn/Breakout_rew.png)     | `python3 atari_qrdqn.py --task "BreakoutNoFrameskip-v4" --n-step 1` |
+| EnduroNoFrameskip-v4      | 1055.9        | ![](results/qrdqn/Enduro_rew.png)     | `python3 atari_qrdqn.py --task "EnduroNoFrameskip-v4"`  |
+| QbertNoFrameskip-v4         | 14990        | ![](results/qrdqn/Qbert_rew.png)        | `python3 atari_qrdqn.py --task "QbertNoFrameskip-v4"`  |
+| MsPacmanNoFrameskip-v4      | 2886        | ![](results/qrdqn/MsPacman_rew.png)     | `python3 atari_qrdqn.py --task "MsPacmanNoFrameskip-v4"`  |
+| SeaquestNoFrameskip-v4      | 5676        | ![](results/qrdqn/Seaquest_rew.png)     | `python3 atari_qrdqn.py --task "SeaquestNoFrameskip-v4"`  |
+| SpaceInvadersNoFrameskip-v4      | 938        | ![](results/qrdqn/SpaceInvader_rew.png)     | `python3 atari_qrdqn.py --task "SpaceInvadersNoFrameskip-v4"`  |
+
 # BCQ
 
 TODO: after the `done` issue fixed, the result should be re-tuned and place here.
@@ -49,4 +63,3 @@ 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`.
-
diff --git a/examples/atari/atari_network.py b/examples/atari/atari_network.py
@@ -64,8 +64,8 @@ def __init__(
         num_atoms: int = 51,
         device: Union[str, int, torch.device] = "cpu",
     ) -> None:
-        super().__init__(c, h, w, [np.prod(action_shape) * num_atoms], device)
-        self.action_shape = action_shape
+        self.action_num = np.prod(action_shape)
+        super().__init__(c, h, w, [self.action_num * num_atoms], device)
         self.num_atoms = num_atoms
 
     def forward(
@@ -77,5 +77,38 @@ def forward(
         r"""Mapping: x -> Z(x, \*)."""
         x, state = super().forward(x)
         x = x.view(-1, self.num_atoms).softmax(dim=-1)
-        x = x.view(-1, np.prod(self.action_shape), self.num_atoms)
+        x = x.view(-1, self.action_num, self.num_atoms)
+        return x, state
+
+
+class QRDQN(DQN):
+    """Reference: Distributional Reinforcement Learning with Quantile \
+    Regression.
+
+    For advanced usage (how to customize the network), please refer to
+    :ref:`build_the_network`.
+    """
+
+    def __init__(
+        self,
+        c: int,
+        h: int,
+        w: int,
+        action_shape: Sequence[int],
+        num_quantiles: int = 200,
+        device: Union[str, int, torch.device] = "cpu",
+    ) -> None:
+        self.action_num = np.prod(action_shape)
+        super().__init__(c, h, w, [self.action_num * num_quantiles], device)
+        self.num_quantiles = num_quantiles
+
+    def forward(
+        self,
+        x: Union[np.ndarray, torch.Tensor],
+        state: Optional[Any] = None,
+        info: Dict[str, Any] = {},
+    ) -> Tuple[torch.Tensor, Any]:
+        r"""Mapping: x -> Z(x, \*)."""
+        x, state = super().forward(x)
+        x = x.view(-1, self.action_num, self.num_quantiles)
         return x, state
diff --git a/examples/atari/atari_qrdqn.py b/examples/atari/atari_qrdqn.py
@@ -0,0 +1,153 @@
+import os
+import torch
+import pprint
+import argparse
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+
+from tianshou.policy import QRDQNPolicy
+from tianshou.env import SubprocVectorEnv
+from tianshou.trainer import offpolicy_trainer
+from tianshou.data import Collector, ReplayBuffer
+
+from atari_network import QRDQN
+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=0)
+    parser.add_argument('--eps-test', type=float, default=0.005)
+    parser.add_argument('--eps-train', type=float, default=1.)
+    parser.add_argument('--eps-train-final', type=float, default=0.05)
+    parser.add_argument('--buffer-size', type=int, default=100000)
+    parser.add_argument('--lr', type=float, default=0.0001)
+    parser.add_argument('--gamma', type=float, default=0.99)
+    parser.add_argument('--num-quantiles', type=int, default=200)
+    parser.add_argument('--n-step', type=int, default=3)
+    parser.add_argument('--target-update-freq', type=int, default=500)
+    parser.add_argument('--epoch', type=int, default=100)
+    parser.add_argument('--step-per-epoch', type=int, default=10000)
+    parser.add_argument('--collect-per-step', type=int, default=10)
+    parser.add_argument('--batch-size', type=int, default=32)
+    parser.add_argument('--training-num', type=int, default=16)
+    parser.add_argument('--test-num', type=int, default=10)
+    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('--watch', default=False, action='store_true',
+                        help='watch the play of pre-trained policy only')
+    return parser.parse_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_qrdqn(args=get_args()):
+    env = make_atari_env(args)
+    args.state_shape = env.observation_space.shape or env.observation_space.n
+    args.action_shape = env.env.action_space.shape or env.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
+    train_envs = SubprocVectorEnv([lambda: make_atari_env(args)
+                                   for _ in range(args.training_num)])
+    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)
+    train_envs.seed(args.seed)
+    test_envs.seed(args.seed)
+    # define model
+    net = QRDQN(*args.state_shape, args.action_shape,
+                args.num_quantiles, args.device)
+    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
+    # define policy
+    policy = QRDQNPolicy(
+        net, optim, args.gamma, args.num_quantiles,
+        args.n_step, target_update_freq=args.target_update_freq
+    ).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 = ReplayBuffer(args.buffer_size, ignore_obs_next=True,
+                          save_only_last_obs=True, stack_num=args.frames_stack)
+    # collector
+    train_collector = Collector(policy, train_envs, buffer)
+    test_collector = Collector(policy, test_envs)
+    # log
+    log_path = os.path.join(args.logdir, args.task, 'qrdqn')
+    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):
+        if env.env.spec.reward_threshold:
+            return mean_rewards >= env.spec.reward_threshold
+        elif 'Pong' in args.task:
+            return mean_rewards >= 20
+        else:
+            return False
+
+    def train_fn(epoch, env_step):
+        # nature DQN setting, linear decay in the first 1M steps
+        if env_step <= 1e6:
+            eps = args.eps_train - env_step / 1e6 * \
+                (args.eps_train - args.eps_train_final)
+        else:
+            eps = args.eps_train_final
+        policy.set_eps(eps)
+        writer.add_scalar('train/eps', eps, global_step=env_step)
+
+    def test_fn(epoch, env_step):
+        policy.set_eps(args.eps_test)
+
+    # watch agent's performance
+    def watch():
+        print("Testing agent ...")
+        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)
+        pprint.pprint(result)
+
+    if args.watch:
+        watch()
+        exit(0)
+
+    # test train_collector and start filling replay buffer
+    train_collector.collect(n_step=args.batch_size * 4)
+    # trainer
+    result = offpolicy_trainer(
+        policy, train_collector, test_collector, args.epoch,
+        args.step_per_epoch, args.collect_per_step, args.test_num,
+        args.batch_size, train_fn=train_fn, test_fn=test_fn,
+        stop_fn=stop_fn, save_fn=save_fn, writer=writer, test_in_train=False)
+
+    pprint.pprint(result)
+    watch()
+
+
+if __name__ == '__main__':
+    test_qrdqn(get_args())
diff --git a/examples/atari/results/qrdqn/Breakout_rew.png b/examples/atari/results/qrdqn/Breakout_rew.png
diff --git a/examples/atari/results/qrdqn/Enduro_rew.png b/examples/atari/results/qrdqn/Enduro_rew.png
diff --git a/examples/atari/results/qrdqn/MsPacman_rew.png b/examples/atari/results/qrdqn/MsPacman_rew.png
diff --git a/examples/atari/results/qrdqn/Pong_rew.png b/examples/atari/results/qrdqn/Pong_rew.png
diff --git a/examples/atari/results/qrdqn/Qbert_rew.png b/examples/atari/results/qrdqn/Qbert_rew.png
diff --git a/examples/atari/results/qrdqn/Seaquest_rew.png b/examples/atari/results/qrdqn/Seaquest_rew.png
diff --git a/examples/atari/results/qrdqn/SpaceInvader_rew.png b/examples/atari/results/qrdqn/SpaceInvader_rew.png
diff --git a/test/discrete/test_qrdqn.py b/test/discrete/test_qrdqn.py
@@ -0,0 +1,136 @@
+import os
+import gym
+import torch
+import pprint
+import argparse
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+
+from tianshou.policy import QRDQNPolicy
+from tianshou.env import DummyVectorEnv
+from tianshou.utils.net.common import Net
+from tianshou.trainer import offpolicy_trainer
+from tianshou.data import Collector, ReplayBuffer, PrioritizedReplayBuffer
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--task', type=str, default='CartPole-v0')
+    parser.add_argument('--seed', type=int, default=1626)
+    parser.add_argument('--eps-test', type=float, default=0.05)
+    parser.add_argument('--eps-train', type=float, default=0.1)
+    parser.add_argument('--buffer-size', type=int, default=20000)
+    parser.add_argument('--lr', type=float, default=1e-3)
+    parser.add_argument('--gamma', type=float, default=0.9)
+    parser.add_argument('--num-quantiles', type=int, default=200)
+    parser.add_argument('--n-step', type=int, default=3)
+    parser.add_argument('--target-update-freq', type=int, default=320)
+    parser.add_argument('--epoch', type=int, default=10)
+    parser.add_argument('--step-per-epoch', type=int, default=1000)
+    parser.add_argument('--collect-per-step', type=int, default=10)
+    parser.add_argument('--batch-size', type=int, default=64)
+    parser.add_argument('--hidden-sizes', type=int,
+                        nargs='*', default=[128, 128, 128, 128])
+    parser.add_argument('--training-num', type=int, default=8)
+    parser.add_argument('--test-num', type=int, default=100)
+    parser.add_argument('--logdir', type=str, default='log')
+    parser.add_argument('--render', type=float, default=0.)
+    parser.add_argument('--prioritized-replay',
+                        action="store_true", default=False)
+    parser.add_argument('--alpha', type=float, default=0.6)
+    parser.add_argument('--beta', type=float, default=0.4)
+    parser.add_argument(
+        '--device', type=str,
+        default='cuda' if torch.cuda.is_available() else 'cpu')
+    args = parser.parse_known_args()[0]
+    return args
+
+
+def test_qrdqn(args=get_args()):
+    env = gym.make(args.task)
+    args.state_shape = env.observation_space.shape or env.observation_space.n
+    args.action_shape = env.action_space.shape or env.action_space.n
+    # train_envs = gym.make(args.task)
+    # you can also use tianshou.env.SubprocVectorEnv
+    train_envs = DummyVectorEnv(
+        [lambda: gym.make(args.task) for _ in range(args.training_num)])
+    # test_envs = gym.make(args.task)
+    test_envs = DummyVectorEnv(
+        [lambda: gym.make(args.task) for _ in range(args.test_num)])
+    # seed
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    train_envs.seed(args.seed)
+    test_envs.seed(args.seed)
+    # model
+    net = Net(args.state_shape, args.action_shape,
+              hidden_sizes=args.hidden_sizes, device=args.device,
+              softmax=False, num_atoms=args.num_quantiles)
+    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
+    policy = QRDQNPolicy(
+        net, optim, args.gamma, args.num_quantiles,
+        args.n_step, target_update_freq=args.target_update_freq
+    ).to(args.device)
+    # buffer
+    if args.prioritized_replay:
+        buf = PrioritizedReplayBuffer(
+            args.buffer_size, alpha=args.alpha, beta=args.beta)
+    else:
+        buf = ReplayBuffer(args.buffer_size)
+    # collector
+    train_collector = Collector(policy, train_envs, buf)
+    test_collector = Collector(policy, test_envs)
+    # policy.set_eps(1)
+    train_collector.collect(n_step=args.batch_size)
+    # log
+    log_path = os.path.join(args.logdir, args.task, 'qrdqn')
+    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 mean_rewards >= env.spec.reward_threshold
+
+    def train_fn(epoch, env_step):
+        # eps annnealing, just a demo
+        if env_step <= 10000:
+            policy.set_eps(args.eps_train)
+        elif env_step <= 50000:
+            eps = args.eps_train - (env_step - 10000) / \
+                40000 * (0.9 * args.eps_train)
+            policy.set_eps(eps)
+        else:
+            policy.set_eps(0.1 * args.eps_train)
+
+    def test_fn(epoch, env_step):
+        policy.set_eps(args.eps_test)
+
+    # trainer
+    result = offpolicy_trainer(
+        policy, train_collector, test_collector, args.epoch,
+        args.step_per_epoch, args.collect_per_step, args.test_num,
+        args.batch_size, train_fn=train_fn, test_fn=test_fn,
+        stop_fn=stop_fn, save_fn=save_fn, writer=writer)
+
+    assert stop_fn(result['best_reward'])
+    if __name__ == '__main__':
+        pprint.pprint(result)
+        # Let's watch its performance!
+        env = gym.make(args.task)
+        policy.eval()
+        policy.set_eps(args.eps_test)
+        collector = Collector(policy, env)
+        result = collector.collect(n_episode=1, render=args.render)
+        print(f'Final reward: {result["rew"]}, length: {result["len"]}')
+
+
+def test_pqrdqn(args=get_args()):
+    args.prioritized_replay = True
+    args.gamma = .95
+    args.seed = 1
+    test_qrdqn(args)
+
+
+if __name__ == '__main__':
+    test_pqrdqn(get_args())
diff --git a/tianshou/policy/__init__.py b/tianshou/policy/__init__.py
@@ -3,6 +3,7 @@
 from tianshou.policy.imitation.base import ImitationPolicy
 from tianshou.policy.modelfree.dqn import DQNPolicy
 from tianshou.policy.modelfree.c51 import C51Policy
+from tianshou.policy.modelfree.qrdqn import QRDQNPolicy
 from tianshou.policy.modelfree.pg import PGPolicy
 from tianshou.policy.modelfree.a2c import A2CPolicy
 from tianshou.policy.modelfree.ddpg import DDPGPolicy
@@ -21,6 +22,7 @@
     "ImitationPolicy",
     "DQNPolicy",
     "C51Policy",
+    "QRDQNPolicy",
     "PGPolicy",
     "A2CPolicy",
     "DDPGPolicy",