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Partially Observable Multi-Agent RL with Enhanced Deep Distributed Recurrent Q-Network

Many real-world problems are naturally modeled as multi-agent problems, and most of them are partially observable. But multi-agent problems will make the environment became nonstationary from the point of view each agent, which causes the combination of experience replay with IQL to be problematic. DDRQN as a method to solve partially observable RL problems, it didn't solve that. In this article we propose a method based on importance sampling and address DDRQN's disadvantages which can't enable memory replay. Results on the SC2LE environment confirm that this method significantly improve performance compared to original DDRQN