  While most approaches to the problem of Inverse Reinforcement Learning (IRL)
focus on estimating a reward function that best explains an expert agent's
policy or demonstrated behavior on a control task, it is often the case that
such behavior is more succinctly represented by a simple reward combined with a
set of hard constraints. In this setting, the agent is attempting to maximize
cumulative rewards subject to these given constraints on their behavior. We
reformulate the problem of IRL on Markov Decision Processes (MDPs) such that,
given a nominal model of the environment and a nominal reward function, we seek
to estimate state, action, and feature constraints in the environment that
motivate an agent's behavior. Our approach is based on the Maximum Entropy IRL
framework, which allows us to reason about the likelihood of an expert agent's
demonstrations given our knowledge of an MDP. Using our method, we can infer
which constraints can be added to the MDP to most increase the likelihood of
observing these demonstrations. We present an algorithm which iteratively
infers the Maximum Likelihood Constraint to best explain observed behavior, and
we evaluate its efficacy using both simulated behavior and recorded data of
humans navigating around an obstacle.
