Welcome to ElegantRL’s documentation!

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ElegantRL is developped for researchers and practitioners with the following advantages:.

  • Lightweight: The core codes <1,000 lines (check elegantrl/tutorial), using PyTorch (train), OpenAI Gym (env), NumPy, Matplotlib (plot).

  • Efficient: more efficient than Ray RLlib in many testing cases.

  • Stable: much more stable than Stable Baseline 3.

ElegantRL implements the following model-free deep reinforcement learning (DRL) algorithms:

  • DDPG, TD3, SAC, A2C, PPO(GAE) for continuous actions

  • DQN, DoubleDQN, D3QN for discrete actions

For the details of DRL algorithms, please check out the educational webpage OpenAI Spinning Up.

Indices and tables