fix pdqn

.github/ISSUE_TEMPLATE.md

@@ -4,13 +4,13 @@
     + [ ] documentation request (i.e. "X is missing from the documentation.")
     + [ ] new feature request
 - [ ] I have visited the [source website], and in particular read the [known issues]
-- [ ] I have searched through the [issue tracker] for duplicates
+- [ ] I have searched through the [issue categories] for duplicates
 - [ ] I have mentioned version numbers, operating system and environment, where applicable:
   ```python
-  import tianshou, sys
-  print(tianshou.__version__, sys.version, sys.platform)
+  import tianshou, torch, sys
+  print(tianshou.__version__, torch.__version__, sys.version, sys.platform)
   ```
 
   [source website]: https://github.com/thu-ml/tianshou/
   [known issues]: https://github.com/thu-ml/tianshou/#faq-and-known-issues
-  [issue tracker]: https://github.com/thu-ml/tianshou/projects/2
+  [issue categories]: https://github.com/thu-ml/tianshou/projects/2

.github/PULL_REQUEST_TEMPLATE.md

@@ -8,13 +8,13 @@
 Less important but also useful:
 
 - [ ] I have visited the [source website], and in particular read the [known issues]
-- [ ] I have searched through the [issue tracker] for duplicates
+- [ ] I have searched through the [issue categories] for duplicates
 - [ ] I have mentioned version numbers, operating system and environment, where applicable:
   ```python
-  import tianshou, sys
-  print(tianshou.__version__, sys.version, sys.platform)
+  import tianshou, torch, sys
+  print(tianshou.__version__, torch.__version__, sys.version, sys.platform)
   ```
 
   [source website]: https://github.com/thu-ml/tianshou
   [known issues]: https://github.com/thu-ml/tianshou/#faq-and-known-issues
-  [issue tracker]: https://github.com/thu-ml/tianshou/projects/2
+  [issue categories]: https://github.com/thu-ml/tianshou/projects/2

README.md

@@ -20,7 +20,7 @@
 - [Policy Gradient (PG)](https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf)
 - [Deep Q-Network (DQN)](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf)
 - [Double DQN (DDQN)](https://arxiv.org/pdf/1509.06461.pdf) with n-step returns
-- [Prioritized DQN (PDQN)](https://arxiv.org/pdf/1511.05952.pdf))
+- [Prioritized DQN (PDQN)](https://arxiv.org/pdf/1511.05952.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

@@ -11,7 +11,7 @@ Welcome to Tianshou!
 * :class:`~tianshou.policy.PGPolicy` `Policy Gradient <https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf>`_
 * :class:`~tianshou.policy.DQNPolicy` `Deep Q-Network <https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf>`_
 * :class:`~tianshou.policy.DQNPolicy` `Double DQN <https://arxiv.org/pdf/1509.06461.pdf>`_ with n-step returns
-* :class:`~tianshou.policy.DQNPolicy` `Prioritized DQN <https://arxiv.org/pdf/1511.05952.pdf`_
+* :class:`~tianshou.policy.DQNPolicy` `Prioritized DQN <https://arxiv.org/pdf/1511.05952.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>`_

test/base/test_buffer.py

@@ -1,5 +1,5 @@
 import numpy as np
-from tianshou.data import ReplayBuffer
+from tianshou.data import ReplayBuffer, PrioritizedReplayBuffer
 
 if __name__ == '__main__':
     from env import MyTestEnv
@@ -47,6 +47,32 @@ def test_stack(size=5, bufsize=9, stack_num=4):
     print(buf)
 
 
+def test_priortized_replaybuffer(size=32, bufsize=15):
+    env = MyTestEnv(size)
+    buf = PrioritizedReplayBuffer(bufsize, 0.5, 0.5)
+    obs = env.reset()
+    action_list = [1] * 5 + [0] * 10 + [1] * 10
+    for i, a in enumerate(action_list):
+        obs_next, rew, done, info = env.step(a)
+        buf.add(obs, a, rew, done, obs_next, info, np.random.randn() - 0.5)
+        obs = obs_next
+        assert np.isclose(np.sum((buf.weight / buf._weight_sum)[:buf._size]),
+                          1, rtol=1e-12)
+        data, indice = buf.sample(len(buf) // 2)
+        if len(buf) // 2 == 0:
+            assert len(data) == len(buf)
+        else:
+            assert len(data) == len(buf) // 2
+        assert len(buf) == min(bufsize, i + 1), print(len(buf), i)
+        assert np.isclose(buf._weight_sum, (buf.weight).sum())
+    data, indice = buf.sample(len(buf) // 2)
+    buf.update_weight(indice, -data.weight / 2)
+    assert np.isclose(buf.weight[indice], np.power(
+        np.abs(-data.weight / 2), buf._alpha)).all()
+    assert np.isclose(buf._weight_sum, (buf.weight).sum())
+
+
 if __name__ == '__main__':
     test_replaybuffer()
     test_stack()
+    test_priortized_replaybuffer(233333, 200000)

test/base/test_prioritized_replay_buffer.py

@@ -1,37 +0,0 @@
-import numpy as np
-from tianshou.data import PrioritizedReplayBuffer
-
-if __name__ == '__main__':
-    from env import MyTestEnv
-else:  # pytest
-    from test.base.env import MyTestEnv
-
-
-def test_replaybuffer(size=32, bufsize=15):
-    env = MyTestEnv(size)
-    buf = PrioritizedReplayBuffer(bufsize, 0.5, 0.5)
-    obs = env.reset()
-    action_list = [1] * 5 + [0] * 10 + [1] * 10
-    for i, a in enumerate(action_list):
-        obs_next, rew, done, info = env.step(a)
-        buf.add(obs, a, rew, done, obs_next, info, np.random.randn()-0.5)
-        obs = obs_next
-        assert np.isclose(np.sum((buf.weight/buf._weight_sum)[:buf._size]), 1,
-                          rtol=1e-12)
-        data, indice = buf.sample(len(buf) // 2)
-        if len(buf)//2 == 0:
-            assert len(data) == len(buf)
-        else:
-            assert len(data) == len(buf)//2
-        assert len(buf) == min(bufsize, i + 1), print(len(buf), i)
-        assert np.isclose(buf._weight_sum, (buf.weight).sum())
-    data, indice = buf.sample(len(buf) // 2)
-    buf.update_weight(indice, -data.weight/2)
-    assert np.isclose(buf.weight[indice], np.power(
-        np.abs(-data.weight/2), buf._alpha)).all()
-    assert np.isclose(buf._weight_sum, (buf.weight).sum())
-
-
-if __name__ == "__main__":
-    test_replaybuffer(233333, 200000)
-    print("pass")

tianshou/__init__.py

@@ -1,7 +1,7 @@
 from tianshou import data, env, utils, policy, trainer, \
     exploration
 
-__version__ = '0.2.1'
+__version__ = '0.2.2'
 __all__ = [
     'env',
     'data',

tianshou/policy/modelfree/dqn.py

@@ -104,12 +104,15 @@ class DQNPolicy(BasePolicy):
             r = batch.returns
             if isinstance(r, np.ndarray):
                 r = torch.tensor(r, device=q.device, dtype=q.dtype)
-            td = r-q
-            buffer.update_weight(indice, td.detach().numpy())
+            td = r - q
+            buffer.update_weight(indice, td.detach().cpu().numpy())
             impt_weight = torch.tensor(batch.impt_weight,
                                        device=q.device, dtype=torch.float)
-            loss = (td.pow(2)*impt_weight).mean()
+            loss = (td.pow(2) * impt_weight).mean()
+            if not hasattr(batch, 'loss'):
                 batch.loss = loss
+            else:
+                batch.loss += loss
         return batch
 
     def forward(self, batch, state=None,