Joshua McClellan

Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance

Multi-Agent Reinforcement Learning (MARL) struggles with sample inefficiency and poor generalization [1]. These challenges are partially due to a lack of structure or inductive bias in the neural networks typically used in learning the policy. One such form of structure that is commonly observed in…