Learn to implement deep reinforcement learning algorithms in 24 hours.

The ElegantRL library is featured with “elegant” in the following aspects:

Lightweight: core codes have less than 1,000 lines, e.g., tutorial.

Efficient: the performance is comparable with Ray RLlib.

Stable: more stable than Stable Baseline 3.

ElegantRL supports state-of-the-art DRL algorithms, including discrete and continuous ones, and provides user-friendly tutorials in Jupyter notebooks.

The ElegantRL implements DRL algorithms under the Actor-Critic framework, where an Agent (a.k.a, a DRL algorithm) consists of an Actor network and a Critic network. Due to the completeness and simplicity of code structure, users are able to easily customize their own agents.