add link

README.md

@@ -33,9 +33,10 @@
 Here is Tianshou's other features:
 
 - Elegant framework, using only ~2000 lines of code
-- Support parallel environment sampling for all algorithms
+- Support parallel environment sampling for all algorithms [Usage](https://tianshou.readthedocs.io/en/latest/tutorials/cheatsheet.html#parallel-sampling)
-- Support recurrent state representation in actor network and critic network (RNN-style training for POMDP)
+- Support recurrent state representation in actor network and critic network (RNN-style training for POMDP) [Usage](https://tianshou.readthedocs.io/en/latest/tutorials/cheatsheet.html#rnn-style-training)
-- Support any type of environment state (e.g. a dict, a self-defined class, ...)
+- Support any type of environment state (e.g. a dict, a self-defined class, ...) [Usage](https://tianshou.readthedocs.io/en/latest/tutorials/cheatsheet.html#user-defined-environment-and-different-state-representation)
+- Support customized training process [Usage](https://tianshou.readthedocs.io/en/latest/tutorials/cheatsheet.html#customize-training-process)
 - Support n-step returns estimation for all Q-learning based algorithms
 
 In Chinese, Tianshou means divinely ordained and is derived to the gift of being born with. Tianshou is a reinforcement learning platform, and the RL algorithm does not learn from humans. So taking "Tianshou" means that there is no teacher to study with, but rather to learn by themselves through constant interaction with the environment.

docs/index.rst

@@ -23,9 +23,10 @@ Welcome to Tianshou!
 Here is Tianshou's other features:
 
 * Elegant framework, using only ~2000 lines of code
-* Support parallel environment sampling for all algorithms
+* Support parallel environment sampling for all algorithms: :ref:`parallel_sampling`
-* Support recurrent state representation in actor network and critic network (RNN-style training for POMDP)
+* Support recurrent state representation in actor network and critic network (RNN-style training for POMDP): :ref:`rnn_training`
-* Support any type of environment state (e.g. a dict, a self-defined class, ...)
+* Support any type of environment state (e.g. a dict, a self-defined class, ...): :ref:`self_defined_env`
+* Support customized training process: :ref:`customize_training`
 * Support n-step returns estimation :meth:`~tianshou.policy.BasePolicy.compute_nstep_return` for all Q-learning based algorithms
 
 中文文档位于 https://tianshou.readthedocs.io/zh/latest/

docs/tutorials/cheatsheet.rst

@@ -19,13 +19,15 @@ Build New Policy
 
 See :class:`~tianshou.policy.BasePolicy`.
 
-.. _parallel_sampling:
+.. _customize_training:
 
 Customize Training Process
 --------------------------
 
 See :ref:`customized_trainer`.
 
+.. _parallel_sampling:
+
 Parallel Sampling
 -----------------
 

tianshou/env/vecenv.py

@@ -97,15 +97,15 @@ class BaseVectorEnv(ABC, gym.Env):
         pass
 
     @abstractmethod
-    def seed(self, seed: Optional[Union[int, List[int]]] = None) -> None:
+    def seed(self, seed: Optional[Union[int, List[int]]] = None) -> List[int]:
         """Set the seed for all environments.
 
         Accept ``None``, an int (which will extend ``i`` to
         ``[i, i + 1, i + 2, ...]``) or a list.
 
-        :return: The list of seeds used in this env's random number generators.
+        :return: The list of seeds used in this env's random number \
-        The first value in the list should be the "main" seed, or the value
+        generators. The first value in the list should be the "main" seed, or \
-        which a reproducer should pass to 'seed'.
+        the value which a reproducer pass to "seed".
         """
         pass
 
@@ -162,7 +162,7 @@ class VectorEnv(BaseVectorEnv):
         self._info = np.stack(self._info)
         return self._obs, self._rew, self._done, self._info
 
-    def seed(self, seed: Optional[Union[int, List[int]]] = None) -> None:
+    def seed(self, seed: Optional[Union[int, List[int]]] = None) -> List[int]:
         if np.isscalar(seed):
             seed = [seed + _ for _ in range(self.env_num)]
         elif seed is None:
@@ -269,7 +269,7 @@ class SubprocVectorEnv(BaseVectorEnv):
                 self._obs[i] = self.parent_remote[i].recv()
             return self._obs
 
-    def seed(self, seed: Optional[Union[int, List[int]]] = None) -> None:
+    def seed(self, seed: Optional[Union[int, List[int]]] = None) -> List[int]:
         if np.isscalar(seed):
             seed = [seed + _ for _ in range(self.env_num)]
         elif seed is None:
@@ -347,7 +347,7 @@ class RayVectorEnv(BaseVectorEnv):
                 self._obs[i] = ray.get(result_obj[_])
         return self._obs
 
-    def seed(self, seed: Optional[Union[int, List[int]]] = None) -> None:
+    def seed(self, seed: Optional[Union[int, List[int]]] = None) -> List[int]:
         if not hasattr(self.envs[0], 'seed'):
             return
         if np.isscalar(seed):