A Crash Course on Reinforcement Learning

03/08/2021 ∙ by Farnaz Adib Yaghmaie, et al. ∙ 111

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.

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Code Repositories

Crash_course_on_RL

This is a self-contained repository to explain two basic Reinforcement (RL) algorithms.


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