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We can usually assume others have goals analogous to our own. This assumptioncan also, at times, be applied to multi-agent games - e.g. Agent 1's attractionto green pellets is analogous to Agent 2's attraction to red pellets. This"analogy" assumption is tied closely to the cognitive process known as empathy.Inspired by empathy, we design a simple and explainable architecture to modelanother agent's action-value function. This involves learning an "ImaginationNetwork" to transform the other agent's observed state in order to produce ahuman-interpretable "empathetic state" which, when presented to the learningagent, produces behaviours that mimic the other agent. Our approach isapplicable to multi-agent scenarios consisting of a single learning agent andother (independent) agents acting according to fixed policies. Thisarchitecture is particularly beneficial for (but not limited to) algorithmsusing a composite value or reward function. We show our method produces betterperformance in multi-agent games, where it robustly estimates the other's modelin different environment configurations. Additionally, we show that theempathetic states are human interpretable, and thus verifiable.

EMOTE: An Explainable architecture for Modelling the Other Through Empathy