Add QR-DQN algorithm (#276)

This is the PR for QR-DQN algorithm: https://arxiv.org/abs/1710.10044

1. add QR-DQN policy in tianshou/policy/modelfree/qrdqn.py.
2. add QR-DQN net in examples/atari/atari_network.py.
3. add QR-DQN atari example in examples/atari/atari_qrdqn.py.
4. add QR-DQN statement in tianshou/policy/init.py.
5. add QR-DQN unit test in test/discrete/test_qrdqn.py.
6. add QR-DQN atari results in examples/atari/results/qrdqn/.
7. add compute_q_value in DQNPolicy and C51Policy for simplify forward function.
8. move `with torch.no_grad():` from `_target_q` to BasePolicy

By running "python3 atari_qrdqn.py --task "PongNoFrameskip-v4" --batch-size 64", get best_result': '19.8 ± 0.40', in epoch 8.

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)

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>`_

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`.
-

examples/atari/atari_network.py

@@ -64,8 +64,8 @@ class C51(DQN):
         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 @@ class C51(DQN):
         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

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())

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())

tianshou/policy/__init__.py

@@ -3,6 +3,7 @@ from tianshou.policy.random import RandomPolicy
 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 @@ __all__ = [
     "ImitationPolicy",
     "DQNPolicy",
     "C51Policy",
+    "QRDQNPolicy",
     "PGPolicy",
     "A2CPolicy",
     "DDPGPolicy",

tianshou/policy/base.py

@@ -257,6 +257,7 @@ class BasePolicy(ABC, nn.Module):
             mean, std = 0.0, 1.0
         buf_len = len(buffer)
         terminal = (indice + n_step - 1) % buf_len
+        with torch.no_grad():
             target_q_torch = target_q_fn(buffer, terminal)  # (bsz, ?)
         target_q = to_numpy(target_q_torch)
 

tianshou/policy/imitation/discrete_bcq.py

@@ -74,7 +74,6 @@ class DiscreteBCQPolicy(DQNPolicy):
     ) -> torch.Tensor:
         batch = buffer[indice]  # batch.obs_next: s_{t+n}
         # target_Q = Q_old(s_, argmax(Q_new(s_, *)))
-        with torch.no_grad():
         act = self(batch, input="obs_next", eps=0.0).act
         target_q, _ = self.model_old(batch.obs_next)
         target_q = target_q[np.arange(len(act)), act]

tianshou/policy/modelfree/c51.py

@@ -1,9 +1,9 @@
 import torch
 import numpy as np
-from typing import Any, Dict, Union, Optional
+from typing import Any, Dict
 
 from tianshou.policy import DQNPolicy
-from tianshou.data import Batch, ReplayBuffer, to_numpy
+from tianshou.data import Batch, ReplayBuffer
 
 
 class C51Policy(DQNPolicy):
@@ -63,46 +63,9 @@ class C51Policy(DQNPolicy):
     ) -> torch.Tensor:
         return self.support.repeat(len(indice), 1)  # shape: [bsz, num_atoms]
 
-    def forward(
-        self,
-        batch: Batch,
-        state: Optional[Union[dict, Batch, np.ndarray]] = None,
-        model: str = "model",
-        input: str = "obs",
-        **kwargs: Any,
-    ) -> Batch:
-        """Compute action over the given batch data.
-
-        :return: A :class:`~tianshou.data.Batch` which has 2 keys:
-
-            * ``act`` the action.
-            * ``state`` the hidden state.
-
-        .. seealso::
-
-            Please refer to :meth:`~tianshou.policy.DQNPolicy.forward` for
-            more detailed explanation.
-        """
-        model = getattr(self, model)
-        obs = batch[input]
-        obs_ = obs.obs if hasattr(obs, "obs") else obs
-        dist, h = model(obs_, state=state, info=batch.info)
-        q = (dist * self.support).sum(2)
-        act: np.ndarray = to_numpy(q.max(dim=1)[1])
-        if hasattr(obs, "mask"):
-            # some of actions are masked, they cannot be selected
-            q_: np.ndarray = to_numpy(q)
-            q_[~obs.mask] = -np.inf
-            act = q_.argmax(axis=1)
-        # add eps to act in training or testing phase
-        if not self.updating and not np.isclose(self.eps, 0.0):
-            for i in range(len(q)):
-                if np.random.rand() < self.eps:
-                    q_ = np.random.rand(*q[i].shape)
-                    if hasattr(obs, "mask"):
-                        q_[~obs.mask[i]] = -np.inf
-                    act[i] = q_.argmax()
-        return Batch(logits=dist, act=act, state=h)
+    def compute_q_value(self, logits: torch.Tensor) -> torch.Tensor:
+        """Compute the q value based on the network's raw output logits."""
+        return (logits * self.support).sum(2)
 
     def _target_dist(self, batch: Batch) -> torch.Tensor:
         if self._target:

tianshou/policy/modelfree/ddpg.py

@@ -102,7 +102,6 @@ class DDPGPolicy(BasePolicy):
         self, buffer: ReplayBuffer, indice: np.ndarray
     ) -> torch.Tensor:
         batch = buffer[indice]  # batch.obs_next: s_{t+n}
-        with torch.no_grad():
         target_q = self.critic_old(
             batch.obs_next,
             self(batch, model='actor_old', input='obs_next').act)

tianshou/policy/modelfree/discrete_sac.py

@@ -78,7 +78,6 @@ class DiscreteSACPolicy(SACPolicy):
         self, buffer: ReplayBuffer, indice: np.ndarray
     ) -> torch.Tensor:
         batch = buffer[indice]  # batch.obs: s_{t+n}
-        with torch.no_grad():
         obs_next_result = self(batch, input="obs_next")
         dist = obs_next_result.dist
         target_q = dist.probs * torch.min(

tianshou/policy/modelfree/dqn.py

@@ -79,7 +79,6 @@ class DQNPolicy(BasePolicy):
     ) -> torch.Tensor:
         batch = buffer[indice]  # batch.obs_next: s_{t+n}
         # target_Q = Q_old(s_, argmax(Q_new(s_, *)))
-        with torch.no_grad():
         if self._target:
             a = self(batch, input="obs_next").act
             target_q = self(
@@ -103,6 +102,10 @@ class DQNPolicy(BasePolicy):
             self._gamma, self._n_step, self._rew_norm)
         return batch
 
+    def compute_q_value(self, logits: torch.Tensor) -> torch.Tensor:
+        """Compute the q value based on the network's raw output logits."""
+        return logits
+
     def forward(
         self,
         batch: Batch,
@@ -143,7 +146,8 @@ class DQNPolicy(BasePolicy):
         model = getattr(self, model)
         obs = batch[input]
         obs_ = obs.obs if hasattr(obs, "obs") else obs
-        q, h = model(obs_, state=state, info=batch.info)
+        logits, h = model(obs_, state=state, info=batch.info)
+        q = self.compute_q_value(logits)
         act: np.ndarray = to_numpy(q.max(dim=1)[1])
         if hasattr(obs, "mask"):
             # some of actions are masked, they cannot be selected
@@ -158,7 +162,7 @@ class DQNPolicy(BasePolicy):
                     if hasattr(obs, "mask"):
                         q_[~obs.mask[i]] = -np.inf
                     act[i] = q_.argmax()
-        return Batch(logits=q, act=act, state=h)
+        return Batch(logits=logits, act=act, state=h)
 
     def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
         if self._target and self._iter % self._freq == 0:

tianshou/policy/modelfree/qrdqn.py

@@ -0,0 +1,94 @@
+import torch
+import warnings
+import numpy as np
+from typing import Any, Dict
+import torch.nn.functional as F
+
+from tianshou.policy import DQNPolicy
+from tianshou.data import Batch, ReplayBuffer
+
+
+class QRDQNPolicy(DQNPolicy):
+    """Implementation of Quantile Regression Deep Q-Network. arXiv:1710.10044.
+
+    :param torch.nn.Module model: a model following the rules in
+        :class:`~tianshou.policy.BasePolicy`. (s -> logits)
+    :param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
+    :param float discount_factor: in [0, 1].
+    :param int num_quantiles: the number of quantile midpoints in the inverse
+        cumulative distribution function of the value, defaults to 200.
+    :param int estimation_step: greater than 1, the number of steps to look
+        ahead.
+    :param int target_update_freq: the target network update frequency (0 if
+        you do not use the target network).
+    :param bool reward_normalization: normalize the reward to Normal(0, 1),
+        defaults to False.
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.policy.DQNPolicy` for more detailed
+        explanation.
+    """
+
+    def __init__(
+        self,
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+        discount_factor: float = 0.99,
+        num_quantiles: int = 200,
+        estimation_step: int = 1,
+        target_update_freq: int = 0,
+        reward_normalization: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(model, optim, discount_factor, estimation_step,
+                         target_update_freq, reward_normalization, **kwargs)
+        assert num_quantiles > 1, "num_quantiles should be greater than 1"
+        self._num_quantiles = num_quantiles
+        tau = torch.linspace(0, 1, self._num_quantiles + 1)
+        self.tau_hat = torch.nn.Parameter(
+            ((tau[:-1] + tau[1:]) / 2).view(1, -1, 1), requires_grad=False)
+        warnings.filterwarnings("ignore", message="Using a target size")
+
+    def _target_q(
+        self, buffer: ReplayBuffer, indice: np.ndarray
+    ) -> torch.Tensor:
+        batch = buffer[indice]  # batch.obs_next: s_{t+n}
+        if self._target:
+            a = self(batch, input="obs_next").act
+            next_dist = self(
+                batch, model="model_old", input="obs_next"
+            ).logits
+        else:
+            next_b = self(batch, input="obs_next")
+            a = next_b.act
+            next_dist = next_b.logits
+        next_dist = next_dist[np.arange(len(a)), a, :]
+        return next_dist  # shape: [bsz, num_quantiles]
+
+    def compute_q_value(self, logits: torch.Tensor) -> torch.Tensor:
+        """Compute the q value based on the network's raw output logits."""
+        return logits.mean(2)
+
+    def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
+        if self._target and self._iter % self._freq == 0:
+            self.sync_weight()
+        self.optim.zero_grad()
+        weight = batch.pop("weight", 1.0)
+        curr_dist = self(batch).logits
+        act = batch.act
+        curr_dist = curr_dist[np.arange(len(act)), act, :].unsqueeze(2)
+        target_dist = batch.returns.unsqueeze(1)
+        # calculate each element's difference between curr_dist and target_dist
+        u = F.smooth_l1_loss(target_dist, curr_dist, reduction="none")
+        huber_loss = (u * (
+            self.tau_hat - (target_dist - curr_dist).detach().le(0.).float()
+        ).abs()).sum(-1).mean(1)
+        loss = (huber_loss * weight).mean()
+        # ref: https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/
+        # blob/master/fqf_iqn_qrdqn/agent/qrdqn_agent.py L130
+        batch.weight = u.detach().abs().sum(-1).mean(1)  # prio-buffer
+        loss.backward()
+        self.optim.step()
+        self._iter += 1
+        return {"loss": loss.item()}

tianshou/policy/modelfree/sac.py

@@ -140,7 +140,6 @@ class SACPolicy(DDPGPolicy):
         self, buffer: ReplayBuffer, indice: np.ndarray
     ) -> torch.Tensor:
         batch = buffer[indice]  # batch.obs: s_{t+n}
-        with torch.no_grad():
         obs_next_result = self(batch, input='obs_next')
         a_ = obs_next_result.act
         target_q = torch.min(

tianshou/policy/modelfree/td3.py

@@ -104,7 +104,6 @@ class TD3Policy(DDPGPolicy):
         self, buffer: ReplayBuffer, indice: np.ndarray
     ) -> torch.Tensor:
         batch = buffer[indice]  # batch.obs: s_{t+n}
-        with torch.no_grad():
         a_ = self(batch, model="actor_old", input="obs_next").act
         dev = a_.device
         noise = torch.randn(size=a_.shape, device=dev) * self._policy_noise