Balancing a CartPole System with Reinforcement Learning -- A Tutorial
Robotics
2020-06-15 v2 Machine Learning
Abstract
In this paper, we provide the details of implementing various reinforcement learning (RL) algorithms for controlling a Cart-Pole system. In particular, we describe various RL concepts such as Q-learning, Deep Q Networks (DQN), Double DQN, Dueling networks, (prioritized) experience replay and show their effect on the learning performance. In the process, the readers will be introduced to OpenAI/Gym and Keras utilities used for implementing the above concepts. It is observed that DQN with PER provides best performance among all other architectures being able to solve the problem within 150 episodes.
Cite
@article{arxiv.2006.04938,
title = {Balancing a CartPole System with Reinforcement Learning -- A Tutorial},
author = {Swagat Kumar},
journal= {arXiv preprint arXiv:2006.04938},
year = {2020}
}
Comments
8 pages, 8 figures, 15 code listings and one table