  Modeling the purposeful behavior of imperfect agents from a small number of
observations is a challenging task. When restricted to the single-agent
decision-theoretic setting, inverse optimal control techniques assume that
observed behavior is an approximately optimal solution to an unknown decision
problem. These techniques learn a utility function that explains the example
behavior and can then be used to accurately predict or imitate future behavior
in similar observed or unobserved situations.
  In this work, we consider similar tasks in competitive and cooperative
multi-agent domains. Here, unlike single-agent settings, a player cannot
myopically maximize its reward --- it must speculate on how the other agents
may act to influence the game's outcome. Employing the game-theoretic notion of
regret and the principle of maximum entropy, we introduce a technique for
predicting and generalizing behavior, as well as recovering a reward function
in these domains.
