Welcome to Tianshou!¶
Tianshou (天授) is a reinforcement learning platform based on pure PyTorch. Unlike existing reinforcement learning libraries, which are mainly based on TensorFlow, have many nested classes, unfriendly API, or slow-speed, Tianshou provides a fast-speed framework and pythonic API for building the deep reinforcement learning agent. The supported interface algorithms include:
ImitationPolicy
Imitation Learning
Here is Tianshou’s other features:
Elegant framework, using only ~2000 lines of code
Support parallel environment simulation (synchronous or asynchronous) for all algorithms: Parallel Sampling
Support recurrent state representation in actor network and critic network (RNN-style training for POMDP): RNN-style Training
Support any type of environment state/action (e.g. a dict, a self-defined class, …): User-defined Environment and Different State Representation
Support Customize Training Process
Support n-step returns estimation
compute_nstep_return()
and prioritized experience replayPrioritizedReplayBuffer
for all Q-learning based algorithms; GAE, nstep and PER are very fast thanks to numba jit function and vectorized numpy operationSupport Multi-Agent RL
Comprehensive unit tests, including functional checking, RL pipeline checking, documentation checking, PEP8 code-style checking, and type checking
中文文档位于 https://tianshou.readthedocs.io/zh/latest/
Installation¶
Tianshou is currently hosted on PyPI and conda-forge. It requires Python >= 3.6.
You can simply install Tianshou from PyPI with the following command:
$ pip install tianshou
If you use Anaconda or Miniconda, you can install Tianshou from conda-forge through the following command:
$ conda -c conda-forge install tianshou
You can also install with the newest version through GitHub:
$ pip install git+https://github.com/thu-ml/tianshou.git@master --upgrade
After installation, open your python console and type
import tianshou
print(tianshou.__version__)
If no error occurs, you have successfully installed Tianshou.
Tianshou is still under development, you can also check out the documents in stable version through tianshou.readthedocs.io/en/stable/.