ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning

The paper introduces ASALT (Adaptive State Alignment for Lateral Transfer), a novel method in multi-agent reinforcement learning (MARL) that addresses the challenge of transferring knowledge between source and target domains with mismatched state-space dimensionalities. ASALT utilizes observation-level and state-level adapters to map observations and states into a shared embedding space, enhancing sample efficiency and global returns in cooperative environments while reducing negative transfer. This advancement is significant for practitioners as it facilitates more effective policy transfer across heterogeneous domains, potentially improving the performance of MARL systems in diverse applications.