A Crash Course on Reinforcement Learning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 48 references that are in the map. First posted on arXiv 8 Mar 2021.
Builds on
7 of 48 references.
References · 7
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2016OpenAI Gymcited by 1,736
- 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 371
- 2018Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulatorcited by 250
- 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 151
- 2019From self-tuning regulators to reinforcement learning and back againcited by 17
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