Finding intrinsic rewards by embodied evolution and constrained reinforcement learning

DOI10.1016/J.NEUNET.2008.09.013zbMath1254.68267OpenAlexW1974142402WikidataQ47232670 ScholiaQ47232670MaRDI QIDQ1932114

Kenji Doya, Eiji Uchibe

Publication date: 17 January 2013

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2008.09.013

zbMATH Keywords

reinforcement learningcyber rodentsembodied evolutionintrinsic and extrinsic rewards

Mathematics Subject Classification ID

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

Related Items (2)

Finding intrinsic rewards by embodied evolution and constrained reinforcement learning ⋮ Efficient Multi-objective Reinforcement Learning via Multiple-gradient Descent with Iteratively Discovered Weight-Vector Sets

Cites Work

• Finding intrinsic rewards by embodied evolution and constrained reinforcement learning
• Guiding exploration by pre-existing knowledge without modifying reward
• OnActor-Critic Algorithms
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