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chainerrl-0.8.0
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ChainerRL, a deep reinforcement learning library
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | chainerrl-0.8.0 |
| نام | chainerrl |
| نسخه کتابخانه | 0.8.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Yasuhiro Fujita |
| ایمیل نویسنده | fujita@preferred.jp |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/chainerrl/ |
| مجوز | MIT License |
<div align="center"><img src="https://raw.githubusercontent.com/chainer/chainerrl/master/assets/ChainerRL.png" width="400"/></div>
# ChainerRL
[](https://travis-ci.org/chainer/chainerrl)
[](https://coveralls.io/github/chainer/chainerrl?branch=master)
[](http://chainerrl.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.python.org/pypi/chainerrl)
ChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using [Chainer](https://github.com/chainer/chainer), a flexible deep learning framework.
## Installation
ChainerRL is tested with 3.5.1+. For other requirements, see [requirements.txt](requirements.txt).
ChainerRL can be installed via PyPI:
```
pip install chainerrl
```
It can also be installed from the source code:
```
python setup.py install
```
Refer to [Installation](http://chainerrl.readthedocs.io/en/latest/install.html) for more information on installation.
## Getting started
You can try [ChainerRL Quickstart Guide](examples/quickstart/quickstart.ipynb) first, or check the [examples](examples) ready for Atari 2600 and Open AI Gym.
For more information, you can refer to [ChainerRL's documentation](http://chainerrl.readthedocs.io/en/latest/index.html).
## Algorithms
| Algorithm | Discrete Action | Continous Action | Recurrent Model | Batch Training | CPU Async Training |
|:----------|:---------------:|:----------------:|:---------------:|:--------------:|:------------------:|
| DQN (including DoubleDQN etc.) | ✓ | ✓ (NAF) | ✓ | ✓ | x |
| Categorical DQN | ✓ | x | ✓ | ✓ | x |
| Rainbow | ✓ | x | ✓ | ✓ | x |
| IQN | ✓ | x | ✓ | ✓ | x |
| DDPG | x | ✓ | ✓ | ✓ | x |
| A3C | ✓ | ✓ | ✓ | ✓ (A2C) | ✓ |
| ACER | ✓ | ✓ | ✓ | x | ✓ |
| NSQ (N-step Q-learning) | ✓ | ✓ (NAF) | ✓ | x | ✓ |
| PCL (Path Consistency Learning) | ✓ | ✓ | ✓ | x | ✓ |
| PPO | ✓ | ✓ | ✓ | ✓ | x |
| TRPO | ✓ | ✓ | ✓ | ✓ | x |
| TD3 | x | ✓ | x | ✓ | x |
| SAC | x | ✓ | x | ✓ | x |
Following algorithms have been implemented in ChainerRL:
- [A2C (Synchronous variant of A3C)](https://openai.com/blog/baselines-acktr-a2c/)
- examples: [[atari (batched)]](examples/atari/train_a2c_ale.py) [[general gym (batched)]](examples/gym/train_a2c_gym.py)
- [A3C (Asynchronous Advantage Actor-Critic)](https://arxiv.org/abs/1602.01783)
- examples: [[atari reproduction]](examples/atari/reproduction/a3c) [[atari]](examples/atari/train_a3c_ale.py) [[general gym]](examples/gym/train_a3c_gym.py)
- [ACER (Actor-Critic with Experience Replay)](https://arxiv.org/abs/1611.01224)
- examples: [[atari]](examples/atari/train_acer_ale.py) [[general gym]](examples/gym/train_acer_gym.py)
- [Asynchronous N-step Q-learning](https://arxiv.org/abs/1602.01783)
- examples: [[atari]](examples/atari/train_nsq_ale.py)
- [Categorical DQN](https://arxiv.org/abs/1707.06887)
- examples: [[atari]](examples/atari/train_categorical_dqn_ale.py) [[general gym]](examples/gym/train_categorical_dqn_gym.py)
- [DQN (Deep Q-Network)](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) (including [Double DQN](https://arxiv.org/abs/1509.06461), [Persistent Advantage Learning (PAL)](https://arxiv.org/abs/1512.04860), Double PAL, [Dynamic Policy Programming (DPP)](http://www.jmlr.org/papers/volume13/azar12a/azar12a.pdf))
- examples: [[atari reproduction]](examples/atari/reproduction/dqn) [[atari]](examples/atari/train_dqn_ale.py) [[atari (batched)]](examples/atari/train_dqn_batch_ale.py) [[flickering atari]](examples/atari/train_drqn_ale.py) [[general gym]](examples/gym/train_dqn_gym.py)
- [DDPG (Deep Deterministic Policy Gradients)](https://arxiv.org/abs/1509.02971) (including [SVG(0)](https://arxiv.org/abs/1510.09142))
- examples: [[mujoco reproduction]](examples/mujoco/reproduction/ddpg) [[mujoco]](examples/mujoco/train_ddpg_gym.py) [[mujoco (batched)]](examples/mujoco/train_ddpg_batch_gym.py)
- [IQN (Implicit Quantile Networks)](https://arxiv.org/abs/1806.06923)
- examples: [[atari reproduction]](examples/atari/reproduction/iqn) [[general gym]](examples/gym/train_iqn_gym.py)
- [PCL (Path Consistency Learning)](https://arxiv.org/abs/1702.08892)
- examples: [[general gym]](examples/gym/train_pcl_gym.py)
- [PPO (Proximal Policy Optimization)](https://arxiv.org/abs/1707.06347)
- examples: [[mujoco reproduction]](examples/mujoco/reproduction/ppo) [[atari]](examples/atari/train_ppo_ale.py) [[mujoco]](examples/mujoco/train_ppo_gym.py) [[mujoco (batched)]](examples/mujoco/train_ppo_batch_gym.py)
- [Rainbow](https://arxiv.org/abs/1710.02298)
- examples: [[atari reproduction]](examples/atari/reproduction/rainbow)
- [REINFORCE](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)
- examples: [[general gym]](examples/gym/train_reinforce_gym.py)
- [SAC (Soft Actor-Critic)](https://arxiv.org/abs/1812.05905)
- examples: [[mujoco reproduction]](examples/mujoco/reproduction/soft_actor_critic)
- [TRPO (Trust Region Policy Optimization)](https://arxiv.org/abs/1502.05477) with [GAE (Generalized Advantage Estimation)](https://arxiv.org/abs/1506.02438)
- examples: [[mujoco]](examples/mujoco/train_trpo_gym.py)
- [TD3 (Twin Delayed Deep Deterministic policy gradient algorithm)](https://arxiv.org/abs/1802.09477)
- examples: [[mujoco reproduction]](examples/mujoco/reproduction/td3)
Following useful techniques have been also implemented in ChainerRL:
- [NoisyNet](https://arxiv.org/abs/1706.10295)
- examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
- [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952)
- examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
- [Dueling Network](https://arxiv.org/abs/1511.06581)
- examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
- [Normalized Advantage Function](https://arxiv.org/abs/1603.00748)
- examples: [[DQN]](examples/gym/train_dqn_gym.py) (for continuous-action envs only)
- [Deep Recurrent Q-Network](https://arxiv.org/abs/1507.06527)
- examples: [[DQN]](examples/atari/train_drqn_ale.py)
## Visualization
ChainerRL has a set of accompanying [visualization tools](https://github.com/chainer/chainerrl-visualizer) in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI.
## Environments
Environments that support the subset of OpenAI Gym's interface (`reset` and `step` methods) can be used.
## Contributing
Any kind of contribution to ChainerRL would be highly appreciated! If you are interested in contributing to ChainerRL, please read [CONTRIBUTING.md](CONTRIBUTING.md).
## License
[MIT License](LICENSE).
## Citations
To cite ChainerRL in publications:
```
@InProceedings{fujita2019chainerrl,
author = {Fujita, Yasuhiro and Kataoka, Toshiki and Nagarajan, Prabhat and Ishikawa, Takahiro},
title = {ChainerRL: A Deep Reinforcement Learning Library},
booktitle = {Workshop on Deep Reinforcement Learning at the 33rd Conference on Neural Information Processing Systems},
location = {Vancouver, Canada},
month = {December},
year = {2019}
}
```
نحوه نصب
نصب پکیج whl chainerrl-0.8.0:
pip install chainerrl-0.8.0.whl
نصب پکیج tar.gz chainerrl-0.8.0:
pip install chainerrl-0.8.0.tar.gz