seealso and change policy dir structure

tianshou/data/batch.py

@@ -15,10 +15,12 @@ class Batch(object):
         >>> data.b
         [5, 5]
         >>> data.b = np.array([3, 4, 5])
-        >>> len(data.b)
-        3
-        >>> data.b[-1]
-        5
+        >>> print(data)
+        Batch(
+            a: 4,
+            b: [3 4 5],
+            c: 2312312,
+        )
 
     In short, you can define a :class:`Batch` with any key-value pair. The
     current implementation of Tianshou typically use 6 keys in

tianshou/data/buffer.py

@@ -9,6 +9,7 @@ class ReplayBuffer(object):
     ``numpy.ndarray``. Here is the usage:
     ::
 
+        >>> import numpy as np
         >>> from tianshou.data import ReplayBuffer
         >>> buf = ReplayBuffer(size=20)
         >>> for i in range(3):
@@ -48,10 +49,15 @@ class ReplayBuffer(object):
         >>> for i in range(16):
         ...     done = i % 5 == 0
         ...     buf.add(obs=i, act=i, rew=i, done=done, obs_next=0, info={})
-        >>> print(buf.obs)
-        [ 9. 10. 11. 12. 13. 14. 15. 7. 8.]
-        >>> print(buf.done)
-        [0. 1. 0. 0. 0. 0. 1. 0. 0.]
+        >>> print(buf)
+        ReplayBuffer(
+            obs: [ 9. 10. 11. 12. 13. 14. 15.  7.  8.],
+            act: [ 9. 10. 11. 12. 13. 14. 15.  7.  8.],
+            rew: [ 9. 10. 11. 12. 13. 14. 15.  7.  8.],
+            done: [0. 1. 0. 0. 0. 0. 1. 0. 0.],
+            obs_next: [0. 0. 0. 0. 0. 0. 0. 0. 0.],
+            info: [{} {} {} {} {} {} {} {} {}],
+        )
         >>> index = np.arange(len(buf))
         >>> print(buf.get_stack(index, 'obs'))
         [[ 7.  7.  8.  9.]
@@ -65,7 +71,7 @@ class ReplayBuffer(object):
          [ 7.  7.  7.  8.]]
         >>> # here is another way to get the stacked data
         >>> # (stack only for obs and obs_next)
-        >>> sum(sum(buf.get_stack(index, 'obs') - buf[index].obs))
+        >>> abs(buf.get_stack(index, 'obs') - buf[index].obs).sum().sum()
         0.0
     """
 
@@ -200,6 +206,11 @@ class ListReplayBuffer(ReplayBuffer):
     """The function of :class:`~tianshou.data.ListReplayBuffer` is almost the
     same as :class:`~tianshou.data.ReplayBuffer`. The only difference is that
     :class:`~tianshou.data.ListReplayBuffer` is based on ``list``.
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.data.ListReplayBuffer` for more
+        detailed explanation.
     """
 
     def __init__(self):

tianshou/env/vecenv.py

@@ -94,8 +94,12 @@ class BaseVectorEnv(ABC, gym.Wrapper):
 
 
 class VectorEnv(BaseVectorEnv):
-    """Dummy vectorized environment wrapper, implemented in for-loop. The usage
-    is in :class:`~tianshou.env.BaseVectorEnv`.
+    """Dummy vectorized environment wrapper, implemented in for-loop.
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
+        explanation.
     """
 
     def __init__(self, env_fns):
@@ -170,8 +174,12 @@ def worker(parent, p, env_fn_wrapper):
 
 
 class SubprocVectorEnv(BaseVectorEnv):
-    """Vectorized environment wrapper based on subprocess. The usage is in
-    :class:`~tianshou.env.BaseVectorEnv`.
+    """Vectorized environment wrapper based on subprocess.
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
+        explanation.
     """
 
     def __init__(self, env_fns):
@@ -247,8 +255,12 @@ class RayVectorEnv(BaseVectorEnv):
     """Vectorized environment wrapper based on
     `ray <https://github.com/ray-project/ray>`_. However, according to our
     test, it is about two times slower than
-    :class:`~tianshou.env.SubprocVectorEnv`. The usage is in
-    :class:`~tianshou.env.BaseVectorEnv`.
+    :class:`~tianshou.env.SubprocVectorEnv`.
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
+        explanation.
     """
 
     def __init__(self, env_fns):

tianshou/policy/__init__.py

@@ -1,11 +1,11 @@
 from tianshou.policy.base import BasePolicy
-from tianshou.policy.dqn import DQNPolicy
-from tianshou.policy.pg import PGPolicy
-from tianshou.policy.a2c import A2CPolicy
-from tianshou.policy.ddpg import DDPGPolicy
-from tianshou.policy.ppo import PPOPolicy
-from tianshou.policy.td3 import TD3Policy
-from tianshou.policy.sac import SACPolicy
+from tianshou.policy.modelfree.dqn import DQNPolicy
+from tianshou.policy.modelfree.pg import PGPolicy
+from tianshou.policy.modelfree.a2c import A2CPolicy
+from tianshou.policy.modelfree.ddpg import DDPGPolicy
+from tianshou.policy.modelfree.ppo import PPOPolicy
+from tianshou.policy.modelfree.td3 import TD3Policy
+from tianshou.policy.modelfree.sac import SACPolicy
 
 __all__ = [
     'BasePolicy',

tianshou/policy/modelfree/a2c.py

@@ -21,6 +21,11 @@ class A2CPolicy(PGPolicy):
     :param float ent_coef: weight for entropy loss, defaults to 0.01.
     :param float max_grad_norm: clipping gradients in back propagation,
         defaults to ``None``.
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
+        explanation.
     """
 
     def __init__(self, actor, critic, optim,
@@ -44,8 +49,10 @@ class A2CPolicy(PGPolicy):
             * ``dist`` the action distribution.
             * ``state`` the hidden state.
 
-        More information can be found at
-        :meth:`~tianshou.policy.BasePolicy.__call__`.
+        .. seealso::
+
+            Please refer to :meth:`~tianshou.policy.BasePolicy.__call__` for
+            more detailed explanation.
         """
         logits, h = self.actor(batch.obs, state=state, info=batch.info)
         if isinstance(logits, tuple):

tianshou/policy/modelfree/ddpg.py

@@ -28,6 +28,11 @@ class DDPGPolicy(BasePolicy):
         defaults to ``False``.
     :param bool ignore_done: ignore the done flag while training the policy,
         defaults to ``False``.
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
+        explanation.
     """
 
     def __init__(self, actor, actor_optim, critic, critic_optim,
@@ -104,8 +109,10 @@ class DDPGPolicy(BasePolicy):
             * ``act`` the action.
             * ``state`` the hidden state.
 
-        More information can be found at
-        :meth:`~tianshou.policy.BasePolicy.__call__`.
+        .. seealso::
+
+            Please refer to :meth:`~tianshou.policy.BasePolicy.__call__` for
+            more detailed explanation.
         """
         model = getattr(self, model)
         obs = getattr(batch, input)

tianshou/policy/modelfree/dqn.py

@@ -18,6 +18,11 @@ class DQNPolicy(BasePolicy):
         ahead.
     :param int target_update_freq: the target network update frequency (``0``
         if you do not use the target network).
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
+        explanation.
     """
 
     def __init__(self, model, optim, discount_factor=0.99,
@@ -106,8 +111,10 @@ class DQNPolicy(BasePolicy):
             * ``logits`` the network's raw output.
             * ``state`` the hidden state.
 
-        More information can be found at
-        :meth:`~tianshou.policy.BasePolicy.__call__`.
+        .. seealso::
+
+            Please refer to :meth:`~tianshou.policy.BasePolicy.__call__` for
+            more detailed explanation.
         """
         model = getattr(self, model)
         obs = getattr(batch, input)

tianshou/policy/modelfree/pg.py

@@ -13,6 +13,11 @@ class PGPolicy(BasePolicy):
     :param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
     :param torch.distributions.Distribution dist_fn: for computing the action.
     :param float discount_factor: in [0, 1].
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
+        explanation.
     """
 
     def __init__(self, model, optim, dist_fn=torch.distributions.Categorical,
@@ -50,8 +55,10 @@ class PGPolicy(BasePolicy):
             * ``dist`` the action distribution.
             * ``state`` the hidden state.
 
-        More information can be found at
-        :meth:`~tianshou.policy.BasePolicy.__call__`.
+        .. seealso::
+
+            Please refer to :meth:`~tianshou.policy.BasePolicy.__call__` for
+            more detailed explanation.
         """
         logits, h = self.model(batch.obs, state=state, info=batch.info)
         if isinstance(logits, tuple):

tianshou/policy/modelfree/ppo.py

@@ -26,6 +26,11 @@ class PPOPolicy(PGPolicy):
     :param float ent_coef: weight for entropy loss, defaults to 0.01.
     :param action_range: the action range (minimum, maximum).
     :type action_range: [float, float]
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
+        explanation.
     """
 
     def __init__(self, actor, critic, optim, dist_fn,
@@ -70,8 +75,10 @@ class PPOPolicy(PGPolicy):
             * ``dist`` the action distribution.
             * ``state`` the hidden state.
 
-        More information can be found at
-        :meth:`~tianshou.policy.BasePolicy.__call__`.
+        .. seealso::
+
+            Please refer to :meth:`~tianshou.policy.BasePolicy.__call__` for
+            more detailed explanation.
         """
         model = getattr(self, model)
         logits, h = model(batch.obs, state=state, info=batch.info)

tianshou/policy/modelfree/sac.py

@@ -33,6 +33,11 @@ class SACPolicy(DDPGPolicy):
         defaults to ``False``.
     :param bool ignore_done: ignore the done flag while training the policy,
         defaults to ``False``.
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
+        explanation.
     """
 
     def __init__(self, actor, actor_optim, critic1, critic1_optim,

tianshou/policy/modelfree/td3.py

@@ -37,6 +37,11 @@ class TD3Policy(DDPGPolicy):
         defaults to ``False``.
     :param bool ignore_done: ignore the done flag while training the policy,
         defaults to ``False``.
+
+    .. seealso::
+
+        Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
+        explanation.
     """
 
     def __init__(self, actor, actor_optim, critic1, critic1_optim,