Learn to implement deep reinforcement learning algorithms in 24 hours.<p>The ElegantRL library is featured with “elegant” in the following aspects:<p>Lightweight: core codes have less than 1,000 lines, e.g., tutorial.<p>Efficient: the performance is comparable with Ray RLlib.<p>Stable: more stable than Stable Baseline 3.<p>ElegantRL supports state-of-the-art DRL algorithms, including discrete and continuous ones, and provides user-friendly tutorials in Jupyter notebooks.<p>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.