hongshaorou/Tianshou
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

polish docs

Browse Source
This commit is contained in:
Trinkle23897 2020-06-03 17:04:26 +08:00
parent dc451dfe88
commit 7bf202f195
2 changed files with 13 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
README.md
View File

@ -30,7 +30,13 @@
- [Prioritized Experience Replay (PER)](https://arxiv.org/pdf/1511.05952.pdf)
- [Generalized Advantage Estimator (GAE)](https://arxiv.org/pdf/1506.02438.pdf)
**Tianshou supports parallel workers for all algorithms as well since all of them are reformatted as replay-buffer based algorithms. All of the algorithms support recurrent state representation in actor network (RNN-style training in POMDP). The environment state can be any type (dict, self-defined class, ...). All Q-learning algorithms support n-step returns estimation.**
Here is Tianshou's other features:
- Elegant framework, using only ~2000 lines of code
- Support parallel environment sampling for all algorithms
- Support recurrent state representation in actor network and critic network (RNN-style training for POMDP)
- Support any type of environment state (e.g. a dict, a self-defined class, ...)
- 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.

7
docs/index.rst
View File

@ -20,8 +20,13 @@ Welcome to Tianshou!
* :class:`~tianshou.data.PrioritizedReplayBuffer` `Prioritized Experience Replay <https://arxiv.org/pdf/1511.05952.pdf>`_
* :meth:`~tianshou.policy.BasePolicy.compute_episodic_return` `Generalized Advantage Estimator <https://arxiv.org/pdf/1506.02438.pdf>`_
Here is Tianshou's other features:
Tianshou supports parallel workers for all algorithms as well since all of them are reformatted as replay-buffer based algorithms. All of the algorithms support recurrent state representation in actor network (RNN-style training in POMDP). The environment state can be any type (Dict, self-defined class, ...).
* Elegant framework, using only ~2000 lines of code
* Support parallel environment sampling for all algorithms
* Support recurrent state representation in actor network and critic network (RNN-style training for POMDP)
* Support any type of environment state (e.g. a dict, a self-defined class, ...)
* 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/