معرفی شرکت ها


chainerrl-0.8.0


Card image cap
تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر
Card image cap
تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر
Card image cap
تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر
Card image cap
تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر
Card image cap
تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

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 [![Build Status](https://travis-ci.org/chainer/chainerrl.svg?branch=master)](https://travis-ci.org/chainer/chainerrl) [![Coverage Status](https://coveralls.io/repos/github/chainer/chainerrl/badge.svg?branch=master)](https://coveralls.io/github/chainer/chainerrl?branch=master) [![Documentation Status](https://readthedocs.org/projects/chainerrl/badge/?version=latest)](http://chainerrl.readthedocs.io/en/latest/?badge=latest) [![PyPI](https://img.shields.io/pypi/v/chainerrl.svg)](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. ![Breakout](assets/breakout.gif) ![Humanoid](assets/humanoid.gif) ![Grasping](assets/grasping.gif) ![Atlas](examples/atlas/assets/atlas.gif) ## 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