強化学習によるCartPole制御のチュートリアル
Balancing a CartPole System with Reinforcement Learning -- A Tutorial
CartPoleを題材にQ学習やDQN、Double DQN、Duelingネットワーク、優先度付き経験再生などの強化学習手法を実装し、学習性能への影響を比較したチュートリアル論文。
著者: Swagat Kumar
分類: cs.RO, cs.LG
原文アブストラクト
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.
