Swarm Reinforcement Learning Algorithm Based on Particle Swarm Optimization Whose Personal Bests Have Lifespans

Authors:
Hitoshi Iima;Yasuaki Kuroe
Affiliations:
Kyoto Institute of Technology, Kyoto, Japan;Kyoto Institute of Technology, Kyoto, Japan
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Year:
2009

Citing 5
Cited 0

Technical Note: \cal Q-Learning

Machine Learning
Swarm intelligence

Swarm intelligence
Reinforcement Learning

Reinforcement Learning
Multi-robot learning with particle swarm optimization

AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A Comprehensive Survey of Multiagent Reinforcement Learning

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

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Abstract

We recently proposed a swarm reinforcement learning algorithm based on particle swarm optimization (PSO) in order to find optimal policies rapidly. In this algorithm, multiple agents are prepared, and they learn not only by individual learning but also by an update procedure of PSO. In this procedure, state-action values are updated based on the personal best and the global best which are found by the agents so far. In this paper, we direct our attention to a problem that overvaluing personal bests brings inferior learning performance. In order not to update the state-action values based on the overvalued personal best, we propose a swarm reinforcement learning algorithm based on PSO in which the personal best of each agent has a lifespan.