MOSAIC for Multiple-Reward Environments

DOI10.1162/NECO_a_00246zbMath1238.68132DBLPjournals/neco/SugimotoHDK12OpenAlexW2099860458WikidataQ39675813 ScholiaQ39675813MaRDI QIDQ2885105

Mitsuo Kawato, Masahiko Haruno, Kenji Doya, Norikazu Sugimoto

Publication date: 21 May 2012

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00246

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Artificial intelligence for robotics (68T40)

Related Items (3)

A differential Hebbian framework for biologically-plausible motor control ⋮ Reward prediction errors, not sensory prediction errors, play a major role in model selection in human reinforcement learning ⋮ From internal models toward metacognitive AI

Cites Work

• Heterarchical reinforcement-learning model for integration of multiple cortico-striatal loops: fMRI examination in stimulus-action-reward association learning
• MOSAIC Model for Sensorimotor Learning and Control
• Multiple Model-Based Reinforcement Learning
• Recent advances in hierarchical reinforcement learning
• Robust Monte Carlo localization for mobile robots

This page was built for publication: MOSAIC for Multiple-Reward Environments