Parallel and distributed multi-agent reinforcement learning

The application of parallel and distributed systems to multi-agent environments has attracted recent attention. Multi-agent systems are a particular type of distributed artificial intelligence system. This paper presents an approach to learning in parallel and distributed systems. A variant of the job assignment problem is chosen as an evaluation task. This is an NP-hard problem, which is relevant to many industrial application domains. Experimental results show the effectiveness of the proposed approach.