Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems

doi:10.1007/s10489-014-0534-0

Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems

2014-05-01

9

PRE

AI

L

Leonardo Anjoletto Ferreira *

C

Carlos H. C. Ribeiro

R

Reinaldo A. C. Bianchi

DOI：10.1007/s10489-014-0534-0

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Abstract

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This article presents two new algorithms for finding the optimal solution of a Multi-agent Multi-objective Reinforcement Learning problem. Both algorithms make use of the concepts of modularization and acceleration by a heuristic function applied in standard Reinforcement Learning algorithms to simplify and speed up the learning process of an agent that learns in a multi-agent multi-objective environment. In order to verify performance of the proposed algorithms, we considered a predator-prey environment in which the learning agent plays the role of prey that must escape the pursuing predator while reaching for food in a fixed location. The results show that combining modularization and acceleration using a heuristics function indeed produced simplification and speeding up of the learning process in a complex problem when comparing with algorithms that do not make use of acceleration or modularization techniques, such as Q-Learning and Minimax-Q.

Keywords:

Reinforcement learning

Heuristically accelerated reinforcement learning

Multi-agent systems

Multi-objective problems

Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems

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