Improving Search Effi ciency in the Action Space of an Instance-Based Reinforcement Learning Technique for Multi-robot Systems (CROSBI ID 37211)
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Podaci o odgovornosti
Yasuda, Toshiyuki ; Ohkura, Kazuhiro
engleski
Improving Search Effi ciency in the Action Space of an Instance-Based Reinforcement Learning Technique for Multi-robot Systems
We have developed a new reinforcement learning technique called Bayesian-discrimination-function-based reinforcement learning (BRL). BRL is unique, in that it not only learns in the predefi ned state and action spaces, but also simultaneously changes their segmentation. BRL has proven to be more eff ective than other standard RL algorithms in dealing with multi-robot system (MRS) problems, where the learning environment is naturally dynamic. This paper introduces an extended form of BRL that improves its learning effi ciency. Instead of generating a random action when a robot encounters an unknown situation, the extended BRL generates an action calculated by a linear interpolation among the rules with high similarity to the current sensory input. In both physical experiments and computer simulations, the extended BRL showed higher search effi ciency than the standard BRL.
Multi-robot System, Reinforcement Learning, Autonomous
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Podaci o prilogu
325-334.
objavljeno
Podaci o knjizi
Advances in Artificial Life
F. Almeida e Costa et al.
Berlin : Heidelberg: Springer
2007.
978-3-540-74912-7