  We address the problem of inverse reinforcement learning in Markov decision
processes where the agent is risk-sensitive. In particular, we model
risk-sensitivity in a reinforcement learning framework by making use of models
of human decision-making having their origins in behavioral psychology,
behavioral economics, and neuroscience. We propose a gradient-based inverse
reinforcement learning algorithm that minimizes a loss function defined on the
observed behavior. We demonstrate the performance of the proposed technique on
two examples, the first of which is the canonical Grid World example and the
second of which is a Markov decision process modeling passengers' decisions
regarding ride-sharing. In the latter, we use pricing and travel time data from
a ride-sharing company to construct the transition probabilities and rewards of
the Markov decision process.
