  The inputs and preferences of human users are important considerations in
situations where these users interact with autonomous cyber or cyber-physical
systems. In these scenarios, one is often interested in aligning behaviors of
the system with the preferences of one or more human users. Cumulative prospect
theory (CPT) is a paradigm that has been empirically shown to model a tendency
of humans to view gains and losses differently. In this paper, we consider a
setting where an autonomous agent has to learn behaviors in an unknown
environment. In traditional reinforcement learning, these behaviors are learned
through repeated interactions with the environment by optimizing an expected
utility. In order to endow the agent with the ability to closely mimic the
behavior of human users, we optimize a CPT-based cost. We introduce the notion
of the CPT-value of an action taken in a state, and establish the convergence
of an iterative dynamic programming-based approach to estimate this quantity.
We develop two algorithms to enable agents to learn policies to optimize the
CPT-vale, and evaluate these algorithms in environments where a target state
has to be reached while avoiding obstacles. We demonstrate that behaviors of
the agent learned using these algorithms are better aligned with that of a
human user who might be placed in the same environment, and is significantly
improved over a baseline that optimizes an expected utility.
