  Seamlessly interacting with humans or robots is hard because these agents are
non-stationary. They update their policy in response to the ego agent's
behavior, and the ego agent must anticipate these changes to co-adapt. Inspired
by humans, we recognize that robots do not need to explicitly model every
low-level action another agent will make; instead, we can capture the latent
strategy of other agents through high-level representations. We propose a
reinforcement learning-based framework for learning latent representations of
an agent's policy, where the ego agent identifies the relationship between its
behavior and the other agent's future strategy. The ego agent then leverages
these latent dynamics to influence the other agent, purposely guiding them
towards policies suitable for co-adaptation. Across several simulated domains
and a real-world air hockey game, our approach outperforms the alternatives and
learns to influence the other agent.
