fix critical bugs in MAPolicy and docs update (#207)

- fix a bug in MAPolicy: `buffer.rew = Batch()` doesn't change `buffer.rew` (thanks mypy)
- polish examples/box2d/bipedal_hardcore_sac.py
- several docs update
- format setup.py and bump version to 0.2.7

README.md

@@ -36,7 +36,7 @@ Here is Tianshou's other features:
 - Elegant framework, using only ~2000 lines of code
 - Support parallel environment simulation (synchronous or asynchronous) 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) [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, ...) [Usage](https://tianshou.readthedocs.io/en/latest/tutorials/cheatsheet.html#user-defined-environment-and-different-state-representation)
+- Support any type of environment state/action (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 and prioritized experience replay for all Q-learning based algorithms; GAE, nstep and PER are very fast thanks to numba jit function and vectorized numpy operation
 - Support multi-agent RL [Usage](https://tianshou.readthedocs.io/en/latest/tutorials/cheatsheet.html##multi-agent-reinforcement-learning)
@@ -74,8 +74,8 @@ $ pip install tianshou
 After installation, open your python console and type
 
 ```python
-import tianshou as ts
-print(ts.__version__)
+import tianshou
+print(tianshou.__version__)
 ```
 
 If no error occurs, you have successfully installed Tianshou.

docs/index.rst

@@ -24,11 +24,11 @@ Welcome to Tianshou!
 Here is Tianshou's other features:
 
 * Elegant framework, using only ~2000 lines of code
-* 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): :ref:`rnn_training`
+* Support parallel environment simulation (synchronous or asynchronous) for all algorithms: :ref:`parallel_sampling`
+* Support recurrent state/action 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, ...): :ref:`self_defined_env`
 * Support customized training process: :ref:`customize_training`
-* Support n-step returns estimation :meth:`~tianshou.policy.BasePolicy.compute_nstep_return` and prioritized experience replay for all Q-learning based algorithms
+* Support n-step returns estimation :meth:`~tianshou.policy.BasePolicy.compute_nstep_return` and prioritized experience replay :class:`~tianshou.data.PrioritizedReplayBuffer` for all Q-learning based algorithms; GAE, nstep and PER are very fast thanks to numba jit function and vectorized numpy operation
 * Support multi-agent RL: :doc:`/tutorials/tictactoe`
 
 中文文档位于 `https://tianshou.readthedocs.io/zh/latest/ <https://tianshou.readthedocs.io/zh/latest/>`_
@@ -63,8 +63,8 @@ If you use Anaconda or Miniconda, you can install Tianshou through the following
 After installation, open your python console and type
 ::
 
-    import tianshou as ts
-    print(ts.__version__)
+    import tianshou
+    print(tianshou.__version__)
 
 If no error occurs, you have successfully installed Tianshou.
 

examples/box2d/README.md

@@ -0,0 +1,7 @@
+# Bipedal-Hardcore-SAC
+
+- Our default choice: remove the done flag penalty, will soon converge to \~250 reward within 100 epochs (10M env steps, 3~4 hours, see the image below)
+- If the done penalty is not removed, it converges much slower than before, about 200 epochs (20M env steps) to reach the same performance (\~200 reward)
+- Action noise is only necessary in the beginning. It is a negative impact at the end of the training. Removing it can reach \~255 (our best result under the original env, no done penalty removed).
+
+![](results/sac/BipedalHardcore.png)

examples/box2d/bipedal_hardcore_sac.py

@@ -24,13 +24,13 @@ def get_args():
     parser.add_argument('--gamma', type=float, default=0.99)
     parser.add_argument('--tau', type=float, default=0.005)
     parser.add_argument('--alpha', type=float, default=0.1)
-    parser.add_argument('--epoch', type=int, default=1000)
-    parser.add_argument('--step-per-epoch', type=int, default=2400)
+    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=128)
     parser.add_argument('--layer-num', type=int, default=1)
     parser.add_argument('--training-num', type=int, default=8)
-    parser.add_argument('--test-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('--rew-norm', type=int, default=0)
@@ -39,14 +39,14 @@ def get_args():
     parser.add_argument(
         '--device', type=str,
         default='cuda' if torch.cuda.is_available() else 'cpu')
+    parser.add_argument('--resume_path', type=str, default=None)
     return parser.parse_args()
 
 
 class EnvWrapper(object):
     """Env wrapper for reward scale, action repeat and action noise"""
 
-    def __init__(self, task, action_repeat=3,
-                 reward_scale=5, act_noise=0.3):
+    def __init__(self, task, action_repeat=3, reward_scale=5, act_noise=0.3):
         self._env = gym.make(task)
         self.action_repeat = action_repeat
         self.reward_scale = reward_scale
@@ -70,8 +70,6 @@ class EnvWrapper(object):
 
 
 def test_sac_bipedal(args=get_args()):
-    torch.set_num_threads(1)  # we just need only one thread for NN
-
     env = EnvWrapper(args.task)
 
     def IsStop(reward):
@@ -118,6 +116,10 @@ def test_sac_bipedal(args=get_args()):
         reward_normalization=args.rew_norm,
         ignore_done=args.ignore_done,
         estimation_step=args.n_step)
+    # load a previous policy
+    if args.resume_path:
+        policy.load_state_dict(torch.load(args.resume_path))
+        print("Loaded agent from: ", args.resume_path)
 
     # collector
     train_collector = Collector(
@@ -135,7 +137,8 @@ def test_sac_bipedal(args=get_args()):
     result = offpolicy_trainer(
         policy, train_collector, test_collector, args.epoch,
         args.step_per_epoch, args.collect_per_step, args.test_num,
-        args.batch_size, stop_fn=IsStop, save_fn=save_fn, writer=writer)
+        args.batch_size, stop_fn=IsStop, save_fn=save_fn, writer=writer,
+        test_in_train=False)
 
     if __name__ == '__main__':
         pprint.pprint(result)

setup.py

@@ -1,12 +1,19 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
+import os
 from setuptools import setup, find_packages
 
 
+def get_version() -> str:
+    # https://packaging.python.org/guides/single-sourcing-package-version/
+    init = open(os.path.join("tianshou", "__init__.py"), "r").read().split()
+    return init[init.index("__version__") + 2][1:-1]
+
+
 setup(
     name='tianshou',
-    version='0.2.6',
+    version=get_version(),
     description='A Library for Deep Reinforcement Learning',
     long_description=open('README.md', encoding='utf8').read(),
     long_description_content_type='text/markdown',

tianshou/__init__.py

@@ -5,7 +5,7 @@ from tianshou import data, env, utils, policy, trainer, exploration
 utils.pre_compile()
 
 
-__version__ = '0.2.6'
+__version__ = '0.2.7'
 
 __all__ = [
     'env',

tianshou/policy/imitation/base.py

@@ -8,7 +8,7 @@ from tianshou.policy import BasePolicy
 
 
 class ImitationPolicy(BasePolicy):
-    """Implementation of vanilla imitation learning (for continuous action space).
+    """Implementation of vanilla imitation learning.
 
     :param torch.nn.Module model: a model following the rules in
         :class:`~tianshou.policy.BasePolicy`. (s -> a)

tianshou/policy/multiagent/mapolicy.py

@@ -36,7 +36,9 @@ class MultiAgentPolicyManager(BasePolicy):
         # reward can be empty Batch (after initial reset) or nparray.
         has_rew = isinstance(buffer.rew, np.ndarray)
         if has_rew:  # save the original reward in save_rew
-            save_rew, buffer.rew = buffer.rew, Batch()
+            # Since we do not override buffer.__setattr__, here we use _meta to
+            # change buffer.rew, otherwise buffer.rew = Batch() has no effect.
+            save_rew, buffer._meta.rew = buffer.rew, Batch()
         for policy in self.policies:
             agent_index = np.nonzero(batch.obs.agent_id == policy.agent_id)[0]
             if len(agent_index) == 0:
@@ -45,11 +47,11 @@ class MultiAgentPolicyManager(BasePolicy):
             tmp_batch, tmp_indice = batch[agent_index], indice[agent_index]
             if has_rew:
                 tmp_batch.rew = tmp_batch.rew[:, policy.agent_id - 1]
-                buffer.rew = save_rew[:, policy.agent_id - 1]
+                buffer._meta.rew = save_rew[:, policy.agent_id - 1]
             results[f'agent_{policy.agent_id}'] = \
                 policy.process_fn(tmp_batch, buffer, tmp_indice)
         if has_rew:  # restore from save_rew
-            buffer.rew = save_rew
+            buffer._meta.rew = save_rew
         return Batch(results)
 
     def forward(self, batch: Batch,