Critical factors in the empirical performance of temporal difference and evolutionary methods for reinforcement learning

19Citations
Citations of this article
60Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Temporal difference and evolutionary methods are two of the most common approaches to solving reinforcement learning problems. However, there is little consensus on their relative merits and there have been few empirical studies that directly compare their performance. This article aims to address this shortcoming by presenting results of empirical comparisons between Sarsa and NEAT, two representative methods, in mountain car and keepaway, two benchmark reinforcement learning tasks. In each task, the methods are evaluated in combination with both linear and nonlinear representations to determine their best configurations. In addition, this article tests two specific hypotheses about the critical factors contributing to these methods' relative performance: (1) that sensor noise reduces the final performance of Sarsa more than that of NEAT, because Sarsa's learning updates are not reliable in the absence of the Markov property and (2) that stochasticity, by introducing noise in fitness estimates, reduces the learning speed of NEAT more than that of Sarsa. Experiments in variations of mountain car and keepaway designed to isolate these factors confirm both these hypotheses. © The Author(s) 2010.

Cite

CITATION STYLE

APA

Whiteson, S., Taylor, M. E., & Stone, P. (2010). Critical factors in the empirical performance of temporal difference and evolutionary methods for reinforcement learning. Autonomous Agents and Multi-Agent Systems, 21(1), 1–35. https://doi.org/10.1007/s10458-009-9100-2

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free