fix #69

.github/workflows/pytest.yml

@@ -17,13 +17,14 @@ jobs:
       uses: actions/setup-python@v1
       with:
         python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install ".[dev]"
     - name: Lint with flake8
       run: |
+        python -m pip install --upgrade pip
+        pip install flake8
         flake8 . --count --show-source --statistics
+    - name: Install dependencies
+      run: |
+        pip install ".[dev]"
     - name: Test with pytest
       run: |
         pytest test --cov tianshou --cov-report=xml --durations 0 -v

CONTRIBUTING.rst

@@ -0,0 +1 @@
+docs/contributing.rst

docs/contributing.rst

@@ -1,14 +1,15 @@
+============
 Contributing
 ============
 
 Install Develop Version
------------------------
+=======================
 
 To install Tianshou in an "editable" mode, run
 
 .. code-block:: bash
 
-    pip3 install -e .
+    pip3 install -e ".[dev]"
 
 in the main directory. This installation is removable by
 
@@ -16,14 +17,8 @@ in the main directory. This installation is removable by
 
     python3 setup.py develop --uninstall
 
-Additional dependencies for developments can be installed by
-
-.. code-block:: bash
-
-    pip3 install ".[dev]"
-
 PEP8 Code Style Check
----------------------
+=====================
 
 We follow PEP8 python code style. To check, in the main directory, run:
 
@@ -32,7 +27,7 @@ We follow PEP8 python code style. To check, in the main directory, run:
     flake8 . --count --show-source --statistics
 
 Test Locally
-------------
+============
 
 This command will run automatic tests in the main directory
 
@@ -41,7 +36,7 @@ This command will run automatic tests in the main directory
     pytest test --cov tianshou -s --durations 0 -v
 
 Test by GitHub Actions
-----------------------
+======================
 
 1. Click the ``Actions`` button in your own repo:
 
@@ -61,7 +56,7 @@ Test by GitHub Actions
     :align: center
 
 Documentation
--------------
+=============
 
 Documentations are written under the ``docs/`` directory as ReStructuredText (``.rst``) files. ``index.rst`` is the main page. A Tutorial on ReStructuredText can be found `here <https://pythonhosted.org/an_example_pypi_project/sphinx.html>`_.
 
@@ -74,3 +69,8 @@ To compile documentation into webpages, run
     make html
 
 under the ``docs/`` directory. The generated webpages are in ``docs/_build`` and can be viewed with browsers.
+
+Chinese Documentation
+=====================
+
+Chinese documentation is in https://github.com/thu-ml/tianshou-docs-zh_CN.

docs/contributor.rst

@@ -1,3 +1,4 @@
+===========
 Contributor
 ===========
 

docs/index.rst

@@ -3,6 +3,7 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
+====================
 Welcome to Tianshou!
 ====================
 
@@ -25,9 +26,9 @@ Tianshou supports parallel workers for all algorithms as well. All of these algo
 
 
 Installation
-------------
+============
 
-Tianshou is currently hosted on `PyPI <https://pypi.org/project/tianshou/>`_. You can simply install Tianshou with the following command:
+Tianshou is currently hosted on `PyPI <https://pypi.org/project/tianshou/>`_. You can simply install Tianshou with the following command (with Python >= 3.6):
 ::
 
     pip3 install tianshou
@@ -86,7 +87,7 @@ Tianshou is still under development, you can also check out the documents in sta
 
 
 Indices and tables
-------------------
+==================
 
 * :ref:`genindex`
 * :ref:`modindex`

docs/tutorials/concepts.rst

@@ -1,3 +1,4 @@
+==========================
 Basic concepts in Tianshou
 ==========================
 
@@ -9,7 +10,7 @@ Tianshou splits a Reinforcement Learning agent training procedure into these par
 
 
 Data Batch
-----------
+==========
 
 .. automodule:: tianshou.data.Batch
    :members:
@@ -17,7 +18,7 @@ Data Batch
 
 
 Data Buffer
------------
+===========
 
 .. automodule:: tianshou.data.ReplayBuffer
    :members:
@@ -28,7 +29,7 @@ Tianshou provides other type of data buffer such as :class:`~tianshou.data.ListR
 .. _policy_concept:
 
 Policy
-------
+======
 
 Tianshou aims to modularizing RL algorithms. It comes into several classes of policies in Tianshou. All of the policy classes must inherit :class:`~tianshou.policy.BasePolicy`.
 
@@ -90,7 +91,7 @@ For other method, you can check out :doc:`/api/tianshou.policy`. We give the usa
 
 
 Collector
----------
+=========
 
 The :class:`~tianshou.data.Collector` enables the policy to interact with different types of environments conveniently.
 In short, :class:`~tianshou.data.Collector` has two main methods:
@@ -106,7 +107,7 @@ The general explanation is listed in :ref:`pseudocode`. Other usages of collecto
 
 
 Trainer
--------
+=======
 
 Once you have a collector and a policy, you can start writing the training method for your RL agent. Trainer, to be honest, is a simple wrapper. It helps you save energy for writing the training loop. You can also construct your own trainer: :ref:`customized_trainer`.
 
@@ -118,7 +119,7 @@ There will be more types of trainers, for instance, multi-agent trainer.
 .. _pseudocode:
 
 A High-level Explanation
-------------------------
+========================
 
 We give a high-level explanation through the pseudocode used in section :ref:`policy_concept`:
 ::
@@ -141,6 +142,6 @@ We give a high-level explanation through the pseudocode used in section :ref:`po
 
 
 Conclusion
-----------
+==========
 
 So far, we go through the overall framework of Tianshou. Really simple, isn't it?

docs/tutorials/dqn.rst

@@ -1,3 +1,4 @@
+==============
 Deep Q Network
 ==============
 
@@ -10,7 +11,7 @@ Contrary to existing Deep RL libraries such as `RLlib <https://github.com/ray-pr
 
 
 Make an Environment
--------------------
+===================
 
 First of all, you have to make an environment for your agent to interact with. For environment interfaces, we follow the convention of `OpenAI Gym <https://github.com/openai/gym>`_. In your Python code, simply import Tianshou and make the environment:
 ::
@@ -24,7 +25,7 @@ CartPole-v0 is a simple environment with a discrete action space, for which DQN
 
 
 Setup Multi-environment Wrapper
--------------------------------
+===============================
 
 It is available if you want the original ``gym.Env``: 
 ::
@@ -44,7 +45,7 @@ For the demonstration, here we use the second block of codes.
 
 
 Build the Network
------------------
+=================
 
 Tianshou supports any user-defined PyTorch networks and optimizers but with the limitation of input and output API. Here is an example code: 
 ::
@@ -80,7 +81,7 @@ The rules of self-defined networks are:
 
 
 Setup Policy
-------------
+============
 
 We use the defined ``net`` and ``optim``, with extra policy hyper-parameters, to define a policy. Here we define a DQN policy with using a target network: 
 ::
@@ -91,7 +92,7 @@ We use the defined ``net`` and ``optim``, with extra policy hyper-parameters, to
 
 
 Setup Collector
----------------
+===============
 
 The collector is a key concept in Tianshou. It allows the policy to interact with different types of environments conveniently. 
 In each step, the collector will let the policy perform (at least) a specified number of steps or episodes and store the data in a replay buffer.
@@ -102,7 +103,7 @@ In each step, the collector will let the policy perform (at least) a specified n
 
 
 Train Policy with a Trainer
----------------------------
+===========================
 
 Tianshou provides :class:`~tianshou.trainer.onpolicy_trainer` and :class:`~tianshou.trainer.offpolicy_trainer`. The trainer will automatically stop training when the policy reach the stop condition ``stop_fn`` on test collector. Since DQN is an off-policy algorithm, we use the :class:`~tianshou.trainer.offpolicy_trainer` as follows:
 ::
@@ -158,7 +159,7 @@ It shows that within approximately 4 seconds, we finished training a DQN agent o
 
 
 Save/Load Policy
-----------------
+================
 
 Since the policy inherits the ``torch.nn.Module`` class, saving and loading the policy are exactly the same as a torch module:
 ::
@@ -168,7 +169,7 @@ Since the policy inherits the ``torch.nn.Module`` class, saving and loading the
 
 
 Watch the Agent's Performance
------------------------------
+=============================
 
 :class:`~tianshou.data.Collector` supports rendering. Here is the example of watching the agent's performance in 35 FPS:
 ::
@@ -181,7 +182,7 @@ Watch the Agent's Performance
 .. _customized_trainer:
 
 Train a Policy with Customized Codes
-------------------------------------
+====================================
 
 "I don't want to use your provided trainer. I want to customize it!"
 

docs/tutorials/trick.rst

@@ -1,3 +1,4 @@
+======================================
 Train a model-free RL agent within 30s
 ======================================
 
@@ -7,7 +8,7 @@ You can also contribute to this page with your own tricks :)
 
 
 Avoid batch-size = 1
---------------------
+====================
 
 In the traditional RL training loop, we always use the policy to interact with only one environment for collecting data. That means most of the time the network use batch-size = 1. Quite inefficient!
 Here is an example of showing how inefficient it is:
@@ -52,7 +53,7 @@ By the way, A2C is better than A3C in some cases: A3C needs to act independently
 
 
 Algorithm specific tricks
--------------------------
+=========================
 
 Here is about the experience of hyper-parameter tuning on CartPole and Pendulum:
 
@@ -66,7 +67,7 @@ Here is about the experience of hyper-parameter tuning on CartPole and Pendulum:
 
 
 Code-level optimization
------------------------
+=======================
 
 Tianshou has many short-but-efficient lines of code. For example, when we want to compute :math:`V(s)` and :math:`V(s')` by the same network, the best way is to concatenate :math:`s` and :math:`s'` together instead of computing the value function using twice of network forward.
 
@@ -74,7 +75,7 @@ Tianshou has many short-but-efficient lines of code. For example, when we want t
 
 
 Finally
--------
+=======
 
 With fast-speed sampling, we could use large batch-size and large learning rate for faster convergence.
 

test/base/env.py

@@ -28,7 +28,7 @@ class MyTestEnv(gym.Env):
         if action == 0:
             self.index = max(self.index - 1, 0)
             if self.dict_state:
-                return {'index': self.index}, 0, False, {}
+                return {'index': self.index}, 0, False, {'key': 1, 'env': self}
             else:
                 return self.index, 0, False, {}
         elif action == 1:
@@ -36,6 +36,7 @@ class MyTestEnv(gym.Env):
             self.done = self.index == self.size
             if self.dict_state:
                 return {'index': self.index}, int(self.done), self.done, \
-                    {'key': 1}
+                    {'key': 1, 'env': self}
             else:
-                return self.index, int(self.done), self.done, {'key': 1}
+                return self.index, int(self.done), self.done, \
+                    {'key': 1, 'env': self}

test/base/test_batch.py

@@ -1,6 +1,6 @@
-import pytest
-import pickle
 import torch
+import pickle
+import pytest
 import numpy as np
 
 from tianshou.data import Batch, to_torch
@@ -15,11 +15,15 @@ def test_batch():
     assert batch.obs == [1, 1]
     assert batch.np.shape == (6, 4)
     assert batch[0].obs == batch[1].obs
-    with pytest.raises(IndexError):
-        batch[2]
     batch.obs = np.arange(5)
     for i, b in enumerate(batch.split(1, shuffle=False)):
-        assert b.obs == batch[i].obs
+        if i != 5:
+            assert b.obs == batch[i].obs
+        else:
+            with pytest.raises(AttributeError):
+                batch[i].obs
+            with pytest.raises(AttributeError):
+                b.obs
     print(batch)
 
 
@@ -95,3 +99,7 @@ def test_batch_from_to_numpy_without_copy():
 if __name__ == '__main__':
     test_batch()
     test_batch_over_batch()
+    test_batch_over_batch_to_torch()
+    test_utils_to_torch()
+    test_batch_pickle()
+    test_batch_from_to_numpy_without_copy()

test/base/test_env.py

@@ -39,7 +39,7 @@ def test_vecenv(size=10, num=8, sleep=0.001):
                     A = v.reset(np.where(C)[0])
                 o.append([A, B, C, D])
             for i in zip(*o):
-                for j in range(1, len(i)):
+                for j in range(1, len(i) - 1):
                     assert (i[0] == i[j]).all()
     else:
         t = [0, 0, 0]

tianshou/data/batch.py

@@ -122,8 +122,11 @@ class Batch:
             return self.__getattr__(index)
         b = Batch()
         for k, v in self.__dict__.items():
-            if k != '_meta' and v is not None:
-                b.__dict__.update(**{k: v[index]})
+            if k != '_meta' and hasattr(v, '__len__'):
+                try:
+                    b.__dict__.update(**{k: v[index]})
+                except IndexError:
+                    continue
         b._meta = self._meta
         return b
 
@@ -238,8 +241,8 @@ class Batch:
 
     def __len__(self) -> int:
         """Return len(self)."""
-        return min([len(v) for k, v in self.__dict__.items()
-                    if k != '_meta' and v is not None])
+        r = [len(v) for k, v in self.__dict__.items() if hasattr(v, '__len__')]
+        return max(r) if len(r) > 0 else 0
 
     def split(self, size: Optional[int] = None,
               shuffle: bool = True) -> Iterator['Batch']:

tianshou/data/buffer.py

@@ -1,7 +1,6 @@
 import pprint
 import numpy as np
-from copy import deepcopy
-from typing import Tuple, Union, Optional
+from typing import Any, Tuple, Union, Optional
 
 from tianshou.data.batch import Batch
 
@@ -60,7 +59,7 @@ class ReplayBuffer(object):
         ReplayBuffer(
             act: array([ 9., 10., 11., 12., 13., 14., 15.,  7.,  8.]),
             done: array([0., 1., 0., 0., 0., 0., 1., 0., 0.]),
-            info: array([{}, {}, {}, {}, {}, {}, {}, {}, {}], dtype=object),
+            info: Batch(),
             obs: Batch(
                      id: array([ 9., 10., 11., 12., 13., 14., 15.,  7.,  8.]),
                  ),
@@ -137,9 +136,7 @@ class ReplayBuffer(object):
             d[k_] = self.__dict__[k__]
         return Batch(**d)
 
-    def _add_to_buffer(
-            self, name: str,
-            inst: Union[dict, Batch, np.ndarray, float, int, bool]) -> None:
+    def _add_to_buffer(self, name: str, inst: Any) -> None:
         if inst is None:
             if getattr(self, name, None) is None:
                 self.__dict__[name] = None
@@ -153,18 +150,17 @@ class ReplayBuffer(object):
                 self.__dict__[name] = np.zeros(
                     (self._maxsize, *inst.shape), dtype=inst.dtype)
             elif isinstance(inst, (dict, Batch)):
-                if name == 'info':
-                    self.__dict__[name] = np.array(
-                        [{} for _ in range(self._maxsize)])
-                else:
-                    if self._meta.get(name, None) is None:
-                        self._meta[name] = list(inst.keys())
-                    for k in inst.keys():
-                        k_ = '_' + name + '@' + k
-                        self._add_to_buffer(k_, inst[k])
-            else:  # assume `inst` is a number
+                if self._meta.get(name, None) is None:
+                    self._meta[name] = list(inst.keys())
+                for k in inst.keys():
+                    k_ = '_' + name + '@' + k
+                    self._add_to_buffer(k_, inst[k])
+            elif np.isscalar(inst):
                 self.__dict__[name] = np.zeros(
                     (self._maxsize,), dtype=np.asarray(inst).dtype)
+            else:  # fall back to np.object
+                self.__dict__[name] = np.array(
+                    [None for _ in range(self._maxsize)])
         if isinstance(inst, np.ndarray) and \
                 self.__dict__[name].shape[1:] != inst.shape:
             raise ValueError(
@@ -172,8 +168,6 @@ class ReplayBuffer(object):
                 f"key: {name}, expect shape: {self.__dict__[name].shape[1:]}, "
                 f"given shape: {inst.shape}.")
         if name not in self._meta:
-            if name == 'info':
-                inst = deepcopy(inst)
             self.__dict__[name][self._index] = inst
 
     def update(self, buffer: 'ReplayBuffer') -> None:
@@ -198,7 +192,7 @@ class ReplayBuffer(object):
             policy: Optional[Union[dict, Batch]] = {},
             **kwargs) -> None:
         """Add a batch of data into replay buffer."""
-        assert isinstance(info, dict), \
+        assert isinstance(info, (dict, Batch)), \
             'You should return a dict in the last argument of env.step().'
         self._add_to_buffer('obs', obs)
         self._add_to_buffer('act', act)
@@ -330,8 +324,6 @@ class ListReplayBuffer(ReplayBuffer):
             return
         if self.__dict__.get(name, None) is None:
             self.__dict__[name] = []
-        if name == 'info':
-            inst = deepcopy(inst)
         self.__dict__[name].append(inst)
 
     def reset(self) -> None: