Package: pfrl
PFRLPFRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python usingPyTorch.InstallationPFRL is tested with Python 3.7.7. For other requirements, seerequirements.txt.PFRL can be installed via PyPI:pip install pfrlIt can also be installed from the source code:python setup.py installRefer toInstallationfor more information on installation.Getting startedYou can tryPFRL Quickstart Guidefirst, or check theexamplesready for Atari 2600 and Open AI Gym.For more information, you can refer toPFRL's documentation.Blog PostsIntroducing PFRL: A PyTorch-based Deep RL LibraryPFRL’s Pretrained Model ZooAlgorithmsAlgorithmDiscrete ActionContinous ActionRecurrent ModelBatch TrainingCPU Async TrainingPretrained models*DQN (including DoubleDQN etc.)✓✓ (NAF)✓✓x✓Categorical DQN✓x✓✓xxRainbow✓x✓✓x✓IQN✓x✓✓x✓DDPGx✓x✓x✓A3C✓✓✓✓ (A2C)✓✓ACER✓✓✓x✓xPPO✓✓✓✓x✓TRPO✓✓✓✓x✓TD3x✓x✓x✓SACx✓x✓x✓*Note on Pretrained models: PFRL provides pretrained models (sometimes called a 'model zoo') for our reproducibility scripts onAtari environments(DQN, IQN, Rainbow, and A3C) andMujoco environments(DDPG, TRPO, PPO, TD3, SAC), for each benchmarked environment.Following algorithms have been implemented in PFRL:A2C (Synchronous variant of A3C)examples:[atari (batched)]A3C (Asynchronous Advantage Actor-Critic)examples:[atari reproduction][atari]ACER (Actor-Critic with Experience Replay)examples:[atari]Categorical DQNexamples:[atari][general gym]DQN (Deep Q-Network)(includingDouble DQN,Persistent Advantage Learning (PAL), Double PAL,Dynamic Policy Programming (DPP))examples:[atari reproduction][atari][atari (batched)][flickering atari][general gym]DDPG (Deep Deterministic Policy Gradients)(includingSVG(0))examples:[mujoco reproduction]IQN (Implicit Quantile Networks)examples:[atari reproduction]PPO (Proximal Policy Optimization)examples:[mujoco reproduction][atari]Rainbowexamples:[atari reproduction][Slime volleyball]REINFORCEexamples:[general gym]SAC (Soft Actor-Critic)examples:[mujoco reproduction][Atlas walk]TRPO (Trust Region Policy Optimization)withGAE (Generalized Advantage Estimation)examples:[mujoco reproduction]TD3 (Twin Delayed Deep Deterministic policy gradient algorithm)examples:[mujoco reproduction]Following useful techniques have been also implemented in PFRL:NoisyNetexamples:[Rainbow][DQN/DoubleDQN/PAL]Prioritized Experience Replayexamples:[Rainbow][DQN/DoubleDQN/PAL]Dueling Networkexamples:[Rainbow][DQN/DoubleDQN/PAL]Normalized Advantage Functionexamples:[DQN](for continuous-action envs only)Deep Recurrent Q-Networkexamples:[DQN]EnvironmentsEnvironments that support the subset of OpenAI Gym's interface (resetandstepmethods) can be used.ContributingAny kind of contribution to PFRL would be highly appreciated! If you are interested in contributing to PFRL, please readCONTRIBUTING.md.LicenseMIT License.CitationsTo cite PFRL in publications, please cite ourpaperon 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}
}