Parallel reinforcement learning with linear function approximation

@inproceedings{Grounds2007ParallelRL,
  title={Parallel reinforcement learning with linear function approximation},
  author={Matthew Jon Grounds and D. Kudenko},
  booktitle={AAMAS '07},
  year={2007}
}
  • Matthew Jon Grounds, D. Kudenko
  • Published in AAMAS '07 2007
  • Computer Science
  • 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. [...] Key Method 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.Expand Abstract
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