Results 311 to 320 of about 5,876,040 (331)

Meta-learning in Reinforcement Learning

Neural Networks, 2003
Meta-parameters in reinforcement learning should be tuned to the environmental dynamics and the animal performance. Here, we propose a biologically plausible meta-reinforcement learning algorithm for tuning these meta-parameters in a dynamic, adaptive manner.
Nicolas Schweighofer, Kenji Doya
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Reinforcement Learning

IFAC Proceedings Volumes, 1997
Reinforcement learning refers to ways of improving performance through trial-and-error experience. Despite recent progress in developing artificial learning systems, including new learning methods for artificial neural networks, most of these systems learn under the tutelage of a knowledgeable “teacher” able to tell them how to respond to a set of ...
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Reinforcement Learning

2008
Just as there are many different types of supervised and unsupervised learning, so there are many different types of reinforcement learning. Reinforcement learning is appropriate for an AI or agent which is actively exploring its environment and also actively exploring what actions are best to take in different situations.
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey   +3 more
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Relational reinforcement learning [PDF]

, 1998
Relational reinforcement learning is presented, a learning technique that combines reinforcement learning with relational learning or inductive logic programming. Due to the use of a more expressive representation language to represent states, actions and q-functions, relational reinforcement learning can be potentially applied to a new range of ...
Džeroski, Sašo, De Raedt, Luc, Driessens, K.   +2 more
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Relational Reinforcement Learning

2001
This paper presents an introduction to reinforcement learning and relational reinforcement learning at a level to be understood by students and researchers with different backgrounds.It gives an overview of the fundamental principles and techniques of reinforcement learning without involving a rigorous deduction of the mathematics involved through the ...
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Reinforcement learning control

Current Opinion in Neurobiology, 1994
Reinforcement learning refers to improving performance through trial-and-error. Despite recent progress in developing artificial learning systems, including new learning methods for artificial neural networks, most of these systems learn under the tutelage of a knowledgeable 'teacher' able to tell them how to respond to a set of training stimuli ...
openaire   +3 more sources

Dense reinforcement learning for safety validation of autonomous vehicles

Nature, 2023
Shuo Feng, Haowei Sun, Xintao Yan, H. Zhu, Zhengxia Zou, Shengyin Shen, Henry X. Liu   +6 more
semanticscholar   +1 more source

Human-level control through deep reinforcement learning

Nature, 2015
Volodymyr Mnih, K. Kavukcuoglu, David Silver, Andrei A. Rusu, J. Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, A. Fidjeland, Georg Ostrovski, Stig Petersen, Charlie Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis   +18 more
semanticscholar   +1 more source

Distributed Reinforcement Learning

Robotics and Autonomous Systems, 1995
In multi-agent systems two forms of learning can be distinguished: centralized learning, that is, learning done by a single agent independent of the other agents; and distributed learning, that is, learning that becomes possible only because several agents are present. Whereas centralized learning has been intensively studied in the field of artificial
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Grandmaster level in StarCraft II using multi-agent reinforcement learning

Nature, 2019
O. Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David Choi, Richard Powell, T. Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, M. Kroiss, Ivo Danihelka, Aja Huang, L. Sifre, Trevor Cai, J. Agapiou, Max Jaderberg, A. Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, D. Budden, Yury Sulsky, James Molloy, T. Paine, Caglar Gulcehre, Ziyun Wang, T. Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, T. Schaul, T. Lillicrap, K. Kavukcuoglu, D. Hassabis, C. Apps, David Silver   +41 more
semanticscholar   +1 more source