The emerging field of Reinforcement Learning (RL) has led to impressive results in varied domains like strategy games, robotics, etc. This handout aims to give a simple introduction to RL from control perspective and discuss three possible approaches to solve an RL problem: Policy Gradient, Policy Iteration, and Model-building. Dynamical systems might have discrete action-space like cartpole where two possible actions are +1 and -1 or continuous action space like linear Gaussian systems. Our discussion covers both cases.

翻译：新兴的强化学习领域(RL)在战略游戏、机器人等不同领域取得了令人印象深刻的成果。 这份手册旨在从控制角度简单介绍RL,并讨论解决RL问题的三种可能办法:政策梯度、政策迭代和模型建设。 动态系统可能拥有离散的行动空间,如马波拉,其中两种可能的行动是+1和-1,或连续行动空间,如线性高斯系统。 我们的讨论涵盖了两种情况。