Affiliations: Lamar University, Computer Science Department, PO Box
10056, Beaumont, Texas, USA. E-mail: [email protected] | Rensselaer Polytechnic Institute, Cognitive Science
Department, 110 Eighth Street, Carnegie 302A, Troy, New York 12180, USA.
E-mail: [email protected]
Abstract: This paper presents a multi-agent reinforcement learning bidding
approach (MARLBS) for performing multi-agent reinforcement learning. MARLBS
integrates reinforcement learning, bidding and genetic algorithms. The general
idea of our multi-agent systems is as follows: There are a number of individual
agents in a team, each agent of the team has two modules: Q module and CQ
module. Each agent can select actions to be performed at each step, which are
done by the Q module. While the CQ module determines at each step whether the
agent should continue or relinquish control. Once an agent relinquishes its
control, a new agent is selected by bidding algorithms. We applied GA-based
MARLBS to the Backgammon game. The experimental results show MARLBS can achieve
a superior level of performance in game-playing, outperforming PubEval, while
the system uses zero built-in knowledge.