In Chinese, Tianshou means divinely ordained and is derived from the gift of being born. Tianshou is a reinforcement learning platform, and the nature of reinforcement learning is to not explicitly learn from a teacher (as in supervised learning) but rather to learn by oneself through constant interaction with the environment. It thus complements the scope of sensAI: The Python Library for Sensible AI which is named after a Japanese term for teacher (“sensei”).

Supported Algorithms

Tianshou supports a wide variety of algorithms:

• Deep Q-Network (DQN)
• Double DQN
• Dueling DQN
• Branching DQN
• Categorical DQN (C51)
• Rainbow DQN (Rainbow)
• Quantile Regression DQN (QRDQN)
• Implicit Quantile Network (IQN)
• Fully-parameterized Quantile Function (FQF)
• Policy Gradient (PG)
• Natural Policy Gradient (NPG)
• Advantage Actor-Critic (A2C)
• Trust Region Policy Optimization (TRPO)
• Proximal Policy Optimization (PPO)
• Deep Deterministic Policy Gradient (DDPG)
• Twin Delayed DDPG (TD3)
• Soft Actor-Critic (SAC)
• Randomized Ensembled Double Q-Learning (REDQ)
• Discrete Soft Actor-Critic (SAC-Discrete)
• Vanilla Imitation Learning
• Batch-Constrained deep Q-Learning (BCQ)
• Conservative Q-Learning (CQL)
• Twin Delayed DDPG with Behavior Cloning (TD3+BC)
• Discrete Batch-Constrained deep Q-Learning (BCQ-Discrete)
• Discrete Conservative Q-Learning (CQL-Discrete)
• Discrete Critic Regularized Regression (CRR-Discrete)
• Generative Adversarial Imitation Learning (GAIL)
• Prioritized Experience Replay (PER)
• Generalized Advantage Estimator (GAE)
• Posterior Sampling Reinforcement Learning (PSRL)
• Intrinsic Curiosity Module (ICM)
• Hindsight Experience Replay (HER)