Metadata-Version: 2.1
Name: pfrl
Version: 0.3.0
Summary: PFRL, a deep reinforcement learning library
Home-page: UNKNOWN
Author: Yasuhiro Fujita
Author-email: fujita@preferred.jp
License: MIT License
Description: <div align="center"><img src="https://raw.githubusercontent.com/pfnet/pfrl/master/assets/PFRL.png" height=150/></div>
        
        # PFRL
        [![Documentation Status](https://readthedocs.org/projects/pfrl/badge/?version=latest)](http://pfrl.readthedocs.io/en/latest/?badge=latest)
        [![PyPI](https://img.shields.io/pypi/v/pfrl.svg)](https://pypi.python.org/pypi/pfrl)
        
        PFRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using [PyTorch](https://github.com/pytorch/pytorch).
        
        ![Boxing](assets/boxing.gif)
        ![Humanoid](assets/humanoid.gif)
        ![Grasping](assets/grasping.gif)
        ![Atlas](examples/atlas/assets/atlas.gif)
        ![SlimeVolley](examples/slimevolley/assets/slimevolley.gif)
        
        ## Installation
        
        PFRL is tested with Python 3.7.7. For other requirements, see [requirements.txt](requirements.txt).
        
        PFRL can be installed via PyPI:
        ```
        pip install pfrl
        ```
        
        It can also be installed from the source code:
        ```
        python setup.py install
        ```
        
        Refer to [Installation](http://pfrl.readthedocs.io/en/latest/install.html) for more information on installation. 
        
        ## Getting started
        
        You can try [PFRL 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 [PFRL's documentation](http://pfrl.readthedocs.io/en/latest/index.html).
        
        ### Blog Posts
        - [Introducing PFRL: A PyTorch-based Deep RL Library](https://t.co/VaT06nejSC?amp=1)
        - [PFRL’s Pretrained Model Zoo](https://bit.ly/3fNx5xH)
        
        ## Algorithms
        
        | Algorithm | Discrete Action | Continous Action | Recurrent Model | Batch Training | CPU Async Training | Pretrained models<sup>*</sup> |
        |:----------|:---------------:|:----------------:|:---------------:|:--------------:|:------------------:|:------------------:|
        | DQN (including DoubleDQN etc.) | ✓ | ✓ (NAF) | ✓ | ✓ | x | ✓ |
        | Categorical DQN | ✓ | x | ✓ | ✓ | x | x |
        | Rainbow | ✓ | x | ✓ | ✓ | x | ✓ |
        | IQN | ✓ | x | ✓ | ✓ | x | ✓ |
        | DDPG | x | ✓ | x | ✓ | x | ✓ |
        | A3C  | ✓ | ✓ | ✓ | ✓ (A2C) | ✓ | ✓ |
        | ACER | ✓ | ✓ | ✓ | x | ✓ | x |
        | PPO  | ✓ | ✓ | ✓ | ✓ | x | ✓ |
        | TRPO | ✓ | ✓ | ✓ | ✓ | x | ✓ |
        | TD3 | x | ✓ | x | ✓ | x | ✓ |
        | SAC | x | ✓ | x | ✓ | x | ✓ |
        
        **<sup>*</sup>Note on Pretrained models**: PFRL provides pretrained models (sometimes called a 'model zoo') for our reproducibility scripts on [Atari environments](https://github.com/pfnet/pfrl/tree/master/examples/atari/reproduction) (DQN, IQN, Rainbow, and A3C) and [Mujoco environments](https://github.com/pfnet/pfrl/tree/master/examples/mujoco/reproduction) (DDPG, TRPO, PPO, TD3, SAC), for each benchmarked environment. 
        
        Following algorithms have been implemented in PFRL:
        - [A2C (Synchronous variant of A3C)](https://openai.com/blog/baselines-acktr-a2c/)
          - examples: [[atari (batched)]](examples/atari/train_a2c_ale.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)
        - [ACER (Actor-Critic with Experience Replay)](https://arxiv.org/abs/1611.01224)
          - examples: [[atari]](examples/atari/train_acer_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)
        - [IQN (Implicit Quantile Networks)](https://arxiv.org/abs/1806.06923)
          - examples: [[atari reproduction]](examples/atari/reproduction/iqn)
        - [PPO (Proximal Policy Optimization)](https://arxiv.org/abs/1707.06347)
          - examples: [[mujoco reproduction]](examples/mujoco/reproduction/ppo) [[atari]](examples/atari/train_ppo_ale.py)
        - [Rainbow](https://arxiv.org/abs/1710.02298)
          - examples: [[atari reproduction]](examples/atari/reproduction/rainbow) [[Slime volleyball]](examples/slimevolley/)
        - [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) [[Atlas walk]](examples/atlas/)
        - [TRPO (Trust Region Policy Optimization)](https://arxiv.org/abs/1502.05477) with [GAE (Generalized Advantage Estimation)](https://arxiv.org/abs/1506.02438)
          - examples: [[mujoco reproduction]](examples/mujoco/reproduction/trpo)
        - [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 PFRL:
        - [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)
        
        
        ## Environments
        
        Environments that support the subset of OpenAI Gym's interface (`reset` and `step` methods) can be used.
        
        ## Contributing
        
        Any kind of contribution to PFRL would be highly appreciated! If you are interested in contributing to PFRL, please read [CONTRIBUTING.md](CONTRIBUTING.md).
        
        ## License
        
        [MIT License](LICENSE).
        
        ## Citations
        
        To cite PFRL in publications, please cite our [paper](https://www.jmlr.org/papers/v22/20-376.html) on ChainerRL, the library on which PFRL is based:
        
        ```
        @article{JMLR:v22:20-376,
          author  = {Yasuhiro Fujita and Prabhat Nagarajan and Toshiki Kataoka and Takahiro Ishikawa},
          title   = {ChainerRL: A Deep Reinforcement Learning Library},
          journal = {Journal of Machine Learning Research},
          year    = {2021},
          volume  = {22},
          number  = {77},
          pages   = {1-14},
          url     = {http://jmlr.org/papers/v22/20-376.html}
        }
        ```
        
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