Double Q($\sigma$) and Q($\sigma, \lambda$): Unifying Reinforcement Learning Control Algorithms
Artificial Intelligence
2017-11-07 v1 Machine Learning
Machine Learning
Abstract
Temporal-difference (TD) learning is an important field in reinforcement learning. Sarsa and Q-Learning are among the most used TD algorithms. The Q() algorithm (Sutton and Barto (2017)) unifies both. This paper extends the Q() algorithm to an online multi-step algorithm Q() using eligibility traces and introduces Double Q() as the extension of Q() to double learning. Experiments suggest that the new Q() algorithm can outperform the classical TD control methods Sarsa(), Q() and Q().
Keywords
Cite
@article{arxiv.1711.01569,
title = {Double Q($\sigma$) and Q($\sigma, \lambda$): Unifying Reinforcement Learning Control Algorithms},
author = {Markus Dumke},
journal= {arXiv preprint arXiv:1711.01569},
year = {2017}
}