Swarm reinforcement learning method based on an actor-critic method

Authors:
Hitoshi Iima;Yasuaki Kuroe
Affiliations:
Kyoto Institute of Technology, Sakyo-ku, Kyoto, Japan;Kyoto Institute of Technology, Sakyo-ku, Kyoto, Japan
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

Citing 6
Cited 0

Technical Note: \cal Q-Learning

Machine Learning
Swarm intelligence

Swarm intelligence
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Reinforcement Learning in Continuous Time and Space

Neural Computation
A Comprehensive Survey of Multiagent Reinforcement Learning

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

Quantified Score

Hi-index	0.00

Visualization

Abstract

We recently proposed swarm reinforcement learning methods in which multiple agents are prepared and they learn not only by individual learning but also by learning through exchanging information among the agents. The methods have been applied to a problem in discrete state-action space so far, and Q-learning method has been used as the individual learning. Although many studies in reinforcement learning have been done for problems in the discrete state-action space, continuous state-action space is required for coping with most real-world tasks. This paper proposes a swarm reinforcement learning method based on an actor-critic method in order to acquire optimal policies rapidly for problems in the continuous state-action space. The proposed method is applied to an inverted pendulum control problem, and its performance is examined through numerical experiments.