Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning
Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …
personalized autonomous driving systems. In this paper, an internal reward function-based …
Towards a comprehensive framework for the multidisciplinary evaluation of organizational maturity on business continuity program management: A systematic …
N Russo, L Reis, C Silveira… - … Security Journal: A Global …, 2024 - Taylor & Francis
Organizational dependency on Information and Communication Technology (ICT) drives the
preparedness challenge to cope with business process disruptions. Business Continuity …
preparedness challenge to cope with business process disruptions. Business Continuity …
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 …
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …
Learning multimodal rewards from rankings
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 …
reward functions. However, expert feedback is often assumed to be drawn from an …
Towards theoretical understanding of inverse reinforcement learning
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 …
reward function justifying the behavior demonstrated by an expert agent. A well-known …
Clare: Conservative model-based reward learning for offline inverse reinforcement learning
This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL),
namely the reward extrapolation error, where the learned reward function may fail to explain …
namely the reward extrapolation error, where the learned reward function may fail to explain …
Anomaly detection and correction of optimizing autonomous systems with inverse reinforcement learning
This article considers autonomous systems whose behaviors seek to optimize an objective
function. This goes beyond standard applications of condition-based maintenance, which …
function. This goes beyond standard applications of condition-based maintenance, which …
Inverse -Learning Using Input–Output Data
This article addresses the problem of learning the objective function of linear discrete-time
systems that use static output-feedback (OPFB) control by designing inverse reinforcement …
systems that use static output-feedback (OPFB) control by designing inverse reinforcement …
Fast lifelong adaptive inverse reinforcement learning from demonstrations
Abstract Learning from Demonstration (LfD) approaches empower end-users to teach robots
novel tasks via demonstrations of the desired behaviors, democratizing access to robotics …
novel tasks via demonstrations of the desired behaviors, democratizing access to robotics …
Inverse contextual bandits: Learning how behavior evolves over time
Understanding a decision-maker's priorities by observing their behavior is critical for
transparency and accountability in decision processes {—} such as in healthcare. Though …
transparency and accountability in decision processes {—} such as in healthcare. Though …