Parallel reinforcement learning with linear function approximation

Matthew Grounds, Daniel Kudenko

Research output: Contribution to conferencePaperpeer-review


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(¿) 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.
Original languageUndefined/Unknown
Publication statusPublished - 2007

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