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

Multi-agent reinforcement learning (MARL) addresses the problem of training multiple agents that pursue collaborative, competitive, or mixed objectives. Prior work has investigated transfer learning between source and target domains in MARL; however, the majority of existing approaches impose the constraint that the dimensionalities of the observation space and the global state space must be identical across domains. In this paper, we introduce a method that explicitly accommodates mismatched st

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ethics.ai (23 June 2026), “ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning,” evidence record 650, https://ethics.ai/record/650 (originally published by arXiv).

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