Reinforcement Learning (RL) introduction, task formulation. Tabular based solutions: dynamic programming, Monte Carlo, temporal‐difference. Function approximation solutions. Policy gradient, proximal policy optimization, deep deterministic policy gradient, Model‐based reinforcement learning. Imitation learning: behavioral cloning, inverse RL, generative adversarial imitation learning. Meta‐learning. Multi‐agent learning, partial observable environments