Parallel reinforcement learning with linear function approximation

Authors:
Matthew Grounds;Daniel Kudenko
Affiliations:
The University of York;The University of York
Venue:
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Year:
2007

Citing 3
Cited 0

Parallel dynamic programming

Advances in parallel algorithms
Convergence Results for Single-Step On-PolicyReinforcement-Learning Algorithms

Machine Learning
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning

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Abstract

In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal policies for single-agent RL problems more quickly by using parallel hardware. Our approach is based on agents using the SARSA(λ) algorithm, with value functions represented using linear function approximators. In our proposed method, each agent learns independently in a separate simulation of the single-agent problem. The agents periodically exchange information extracted from the weights of their approximators, accelerating convergence towards the optimal policy. We present empirical results for an implementation on a Beowulf cluster.