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Tan Hin Khai Stephen


CS3244-04

You Play Ball, I Play Ball: Bayesian Multi-Agent Reinforcement Learning for Slime Volleyball

Deep reinforcement learning (DRL) has recently shown promising results in multi-agent gameplay. However, multi-agent training imposes heavy data requirements on the already sample-inefficient reinforcement learning paradigm. Bayesian methods are capable of quantifying uncertainty in machine learning, and in the context of sparse data it seems a natural complement to multi-agent reinforcement learning (MARL). Therefore, the aim of our project is to investigate the extent to which Bayesian MARL can alleviate the difficulties in DRL.