Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2006.04222v3
• Date: Fri, 11 Jun 2021 18:53:47 GMT
• Title: Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
• Authors: Shariq Iqbal, Christian A. Schroeder de Witt, Bei Peng, Wendelin B\"ohmer, Shimon Whiteson, Fei Sha
• Abstract summary: Multi-agent settings in the real world often involve tasks with varying types and quantities of agents and non-agent entities. Our method aims to leverage these commonalities by asking the question: What is the expected utility of each agent when only considering a randomly selected sub-group of its observed entities?''
• Score: 59.62721526353915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent settings in the real world often involve tasks with varying types and quantities of agents and non-agent entities; however, common patterns of behavior often emerge among these agents/entities. Our method aims to leverage these commonalities by asking the question: ``What is the expected utility of each agent when only considering a randomly selected sub-group of its observed entities?'' By posing this counterfactual question, we can recognize state-action trajectories within sub-groups of entities that we may have encountered in another task and use what we learned in that task to inform our prediction in the current one. We then reconstruct a prediction of the full returns as a combination of factors considering these disjoint groups of entities and train this ``randomly factorized" value function as an auxiliary objective for value-based multi-agent reinforcement learning. By doing so, our model can recognize and leverage similarities across tasks to improve learning efficiency in a multi-task setting. Our approach, Randomized Entity-wise Factorization for Imagined Learning (REFIL), outperforms all strong baselines by a significant margin in challenging multi-task StarCraft micromanagement settings.

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