# Inverse Risk-Sensitive Reinforcement Learning

## TL;DR

This paper introduces a gradient-based inverse reinforcement learning method for risk-sensitive agents, leveraging behavioral models to infer underlying preferences from observed decision-making in complex scenarios.

## Contribution

It presents a novel inverse reinforcement learning algorithm that accounts for risk sensitivity, integrating behavioral psychology insights into the modeling process.

## Key findings

- Effective in Grid World example
- Successfully modeled ride-sharing decision data
- Demonstrates applicability to real-world scenarios

## Abstract

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.