New Jun Jie (Jet)
NUS Computer Science + USP. Researching reinforcement learning. Ex-Intern at Grab, IMDA. Presented 13th, 15th, 17th STePS.
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.