  We can usually assume others have goals analogous to our own. This assumption
can also, at times, be applied to multi-agent games - e.g. Agent 1's attraction
to 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 model
another agent's action-value function. This involves learning an "Imagination
Network" to transform the other agent's observed state in order to produce a
human-interpretable "empathetic state" which, when presented to the learning
agent, produces behaviours that mimic the other agent. Our approach is
applicable to multi-agent scenarios consisting of a single learning agent and
other (independent) agents acting according to fixed policies. This
architecture is particularly beneficial for (but not limited to) algorithms
using a composite value or reward function. We show our method produces better
performance in multi-agent games, where it robustly estimates the other's model
in different environment configurations. Additionally, we show that the
empathetic states are human interpretable, and thus verifiable.
