Evaluating Q-learning policies for multi-objective foraging task in a multi-agent environment

Authors:
M. Yogeswaran;S. G. Ponnambalam
Affiliations:
School of Engineering, Monash University, Petaling Jaya, Selangor, Malaysia;School of Engineering, Monash University, Petaling Jaya, Selangor, Malaysia
Venue:
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part II
Year:
2010

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Abstract

This paper evaluates the performances of the reported q-learning policies for multi-agent systems. A set of extensively used policies were identified in the open literature namely greedy, ε-greedy, Boltzmann Distribution, Simulated Annealing and Probabiliy Matching. Five agents are modeled to search and retrieve pucks back to a home location in the environment under specified constraints. A number of simulation-based experiments was conducted and based on the numerical results that was obtained, the performances of the learning policies are discussed.