Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning

Z Huang, J Wu, C Lv - IEEE transactions on intelligent …, 2021 - ieeexplore.ieee.org
Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …

Inverse reinforcement learning as the algorithmic basis for theory of mind: current methods and open problems

J Ruiz-Serra, MS Harré - Algorithms, 2023 - mdpi.com
Theory of mind (ToM) is the psychological construct by which we model another's internal
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …

Towards theoretical understanding of inverse reinforcement learning

AM Metelli, F Lazzati, M Restelli - … Conference on Machine …, 2023 - proceedings.mlr.press
Inverse reinforcement learning (IRL) denotes a powerful family of algorithms for recovering a
reward function justifying the behavior demonstrated by an expert agent. A well-known …

Learning multimodal rewards from rankings

V Myers, E Biyik, N Anari… - Conference on robot …, 2022 - proceedings.mlr.press
Learning from human feedback has shown to be a useful approach in acquiring robot
reward functions. However, expert feedback is often assumed to be drawn from an …

Inverse contextual bandits: Learning how behavior evolves over time

A Hüyük, D Jarrett… - … Conference on Machine …, 2022 - proceedings.mlr.press
Understanding a decision-maker's priorities by observing their behavior is critical for
transparency and accountability in decision processes {—} such as in healthcare. Though …