Solving multi-agent control problems using particle swarm optimization
This paper outlines an approximate algorithm for finding an optimal decentralized control in multi-agent systems. Decentralized Partially Observable Markov Decision Processes and their extension to infinite state, observation and action spaces are utilized as a theoretical framework. In the presented algorithm, policies of each agent are represented by a feedforward neural network. Then, a search is performed in a joint weight space of all networks. Particle Swarm Optimization is applied as a search algorithm. Experimental results are provided showing that the algorithm finds good solutions for the classical Tiger Problem extended to multi-agent systems, as well as for a multi-agent navigation task involving large state and action spaces. © 2007 IEEE.
