Abstract

Learning interactive multi-agent behaviors from scratch is often sample-inefficient and fails to exploit reusable skills learned in simpler settings. While latent skill representations enable efficient single-agent reinforcement learning, their extension to multi-agent interaction requires conditioning behaviors on other agents without destroying pretrained structure. We formulate multi-agent interaction as a latent adaptation problem and propose the Latent Motion Adjuster (LMA), a lightweight conditional module that modifies latent actions produced by a pretrained single-agent policy based on other agents’ states. Rather than relearning policies from scratch, our method performs structured residual adaptation in latent space, enabling efficient skill reuse under both cooperative and competitive scenarios. Experiments on physics-based control benchmarks demonstrate that latent-space adaptation improves sample efficiency and interaction performance over fine-tuning and strategic baselines. These results suggest that conditional latent modulation provides a principled mechanism for transferring single-agent skills to multi-agent reinforcement learning.