Agent-Centric Representations for Multi-Agent Reinforcement Learning

Understand

Object-centric representations have recently enabled significant progress in tackling relational reasoning tasks.

• By building a strong object-centric inductive bias into neural architectures, recent efforts have improved generalization and data efficiency of machine learning algorithms for these problems.
• One problem class involving relational reasoning that still remains under-explored is multi-agent reinforcement learning (MARL).
• Here we investigate whether object-centric representations are also beneficial in the fully cooperative MARL setting.