Artificial Mirror Neurons: Modelling Others through Like-Me Inference in Multi-Agent Reinforcement Learning

Humans can understand others partly by using their own experience as a reference, a principle known as Like-Me inference (LIMI). We investigate whether this principle provides a computational mechanism for partner modelling in multi-agent reinforcement learning. We propose a simple LIMI architecture that represents another agent in the ego agent's own embedding space by combining an estimate of the partner's perspective with the ego's own representation mechanism. We study two variants: a fixed perspective transform that requires no behavioural-prediction objective, and a learnt transform that learns the self–other mapping from partner behaviour. We further show theoretically that the benefit of adapting to a partner is bounded by how much partner information is encoded in the agent's hidden state. Across Overcooked, multi-robot coordination, and embodied locomotion tasks, LIMI improves ad hoc teamwork in several settings, particularly when coordinating with unseen partners is difficult. Probing frozen representations further shows that LIMI increases information about partners' actions and internal states. Representational similarity analysis also reveals partial alignment between how self and other encoders in learnt-perspective-transform agents organise the same events, consistent with mirror-like representational structure. Together, our results suggest that reusing one's own representations provides a simple mechanism for implicit partner modelling and behavioural generalisation.