Results 81 to 90 of about 88,537 (171)

Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making

International Journal of Advanced Robotic Systems, 2018
There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions.
Hongbo Gao, Guanya Shi, Guotao Xie, Bo Cheng   +3 more
doaj   +1 more source

Expert-Trajectory-Based Features for Apprenticeship Learning via Inverse Reinforcement Learning for Robotic Manipulation

Applied Sciences
This paper explores the application of Inverse Reinforcement Learning (IRL) in robotics, focusing on inferring reward functions from expert demonstrations of robot arm manipulation tasks.
Francisco J. Naranjo-Campos, Juan G. Victores, Carlos Balaguer   +2 more
doaj   +1 more source

Structure-Based Inverse Reinforcement Learning for Quantification of Biological Knowledge. [PDF]

IEEE Conf Artif Intell, 2023
Ravari A, Ghoreishi SF, Imani M.
europepmc   +1 more source

Interaction-limited Inverse Reinforcement Learning

, 2020
This paper proposes an inverse reinforcement learning (IRL) framework to accelerate learning when the learner-teacher \textit{interaction} is \textit{limited} during training. Our setting is motivated by the realistic scenarios where a helpful teacher is not available or when the teacher cannot access the learning dynamics of the student.
Troussard, Martin, Pignat, Emmanuel, Kamalaruban, Parameswaran, Calinon, Sylvain, Cevher, Volkan   +4 more
openaire   +2 more sources

An Application of Inverse Reinforcement Learning to Estimate Interference in Drone Swarms. [PDF]

Entropy (Basel), 2022
Kim KJ, Santos E, Nguyen H, Pieper S.
europepmc   +1 more source

Hippocampus and striatum show distinct contributions to longitudinal changes in value-based learning in middle childhood

eLife
The hippocampal-dependent memory system and striatal-dependent memory system modulate reinforcement learning depending on feedback timing in adults, but their contributions during development remain unclear.
Johannes Falck, Lei Zhang, Laurel Raffington, Johannes Julius Mohn, Jochen Triesch, Christine Heim, Yee Lee Shing   +6 more
doaj   +1 more source

Human-like Decision Making for Autonomous Vehicles at the Intersection Using Inverse Reinforcement Learning. [PDF]

Sensors (Basel), 2022
Wu Z, Qu F, Yang L, Gong J.
europepmc   +1 more source

Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning. [PDF]

Neuron Behav Data Anal Theory, 2021
Zelinsky GJ, Chen Y, Ahn S, Adeli H, Yang Z, Huang L, Samaras D, Hoai M.   +7 more
europepmc   +1 more source

Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials. [PDF]

Commun Biol, 2021
Batzianoulis I, Iwane F, Wei S, Correia CGPR, Chavarriaga R, Millán JDR, Billard A.   +6 more
europepmc   +1 more source

Inverse reinforcement learning from failure

, 2016
Inverse reinforcement learning (IRL) allows autonomous agents to learn to solve complex tasks from successful demonstrations. However, in many settings, e.g., when a human learns the task by trial and error, failed demonstrations are also readily available.
Shiarlis, K, Messias, J, Whiteson, S
openaire   +2 more sources