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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
PFRL: a PyTorch-based deep reinforcement learning library
| Date | Stars |
|---|---|
| 2026-07-31 | 1273 |
| 2026-08-03 | 1273 |
| 2026-08-04 | 1273 |
| 2026-08-06 | 1273 |
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<div align="center"><img src="https://raw.githubusercontent.com/pfnet/pfrl/master/assets/PFRL.png" height=150/></div> # PFRL [](http://pfrl.readthedocs.io/en/latest/?badge=latest) [](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).      ## 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 | Continuous 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 (D
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Yasuhiro Fujita · @pfnet
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0d9f3ff93b7ce3fd, desc:reinforcement learning