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