Package: chainerrl
ChainerRLChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python usingChainer, a flexible deep learning framework.InstallationChainerRL is tested with 3.5.1+. For other requirements, seerequirements.txt.ChainerRL can be installed via PyPI:pip install chainerrlIt can also be installed from the source code:python setup.py installRefer toInstallationfor more information on installation.Getting startedYou can tryChainerRL Quickstart Guidefirst, or check theexamplesready for Atari 2600 and Open AI Gym.For more information, you can refer toChainerRL's documentation.AlgorithmsAlgorithmDiscrete ActionContinous ActionRecurrent ModelBatch TrainingCPU Async TrainingDQN (including DoubleDQN etc.)✓✓ (NAF)✓✓xCategorical DQN✓x✓✓xRainbow✓x✓✓xIQN✓x✓✓xDDPGx✓✓✓xA3C✓✓✓✓ (A2C)✓ACER✓✓✓x✓NSQ (N-step Q-learning)✓✓ (NAF)✓x✓PCL (Path Consistency Learning)✓✓✓x✓PPO✓✓✓✓xTRPO✓✓✓✓xTD3x✓x✓xSACx✓x✓xFollowing algorithms have been implemented in ChainerRL:A2C (Synchronous variant of A3C)examples:[atari (batched)][general gym (batched)]A3C (Asynchronous Advantage Actor-Critic)examples:[atari reproduction][atari][general gym]ACER (Actor-Critic with Experience Replay)examples:[atari][general gym]Asynchronous N-step Q-learningexamples:[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][mujoco][mujoco (batched)]IQN (Implicit Quantile Networks)examples:[atari reproduction][general gym]PCL (Path Consistency Learning)examples:[general gym]PPO (Proximal Policy Optimization)examples:[mujoco reproduction][atari][mujoco][mujoco (batched)]Rainbowexamples:[atari reproduction]REINFORCEexamples:[general gym]SAC (Soft Actor-Critic)examples:[mujoco reproduction]TRPO (Trust Region Policy Optimization)withGAE (Generalized Advantage Estimation)examples:[mujoco]TD3 (Twin Delayed Deep Deterministic policy gradient algorithm)examples:[mujoco reproduction]Following useful techniques have been also implemented in ChainerRL: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]VisualizationChainerRL has a set of accompanyingvisualization toolsin 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.EnvironmentsEnvironments that support the subset of OpenAI Gym's interface (resetandstepmethods) can be used.ContributingAny kind of contribution to ChainerRL would be highly appreciated! If you are interested in contributing to ChainerRL, please readCONTRIBUTING.md.LicenseMIT License.CitationsTo 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}
}