Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field.
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Neural Forecasting Systems 001 Takashi Kuremoto Masanao Obayashi and Kunikazu Kobayashi
Reinforcement learning in system identification 021 Mariela Cerrada and Jose Aguilar
Reinforcement Evolutionary Learning for NeuroFuzzy Controller Design 033 ChengJian
Reinforcement Learning and Quantum Reinforcement Learning 059 ChunLin Chen and DaoYi Dong 5 An Extension of Finitestate Markov
Decision Process and an Application of Grammatical Inference 085 Takeshi Shibata and Ryo Yoshinaka 6 Interaction between the SpatioTemporal L...
A cellular mechanism of reinforcement learning 105 Minoru Tsukada 7 Reinforcement Learning Embedded in Brains and Robots 119 Cornelius We...
Modular Learning Systems
Behavior Acquisition in MultiAgent Environment 225 Yasutake Takahashi and Minoru Asada
Strategies within the Reinforcement Learning Paradigm 239 Olivier Pietquin
River Basin Using Adaptive Neural Fuzzy Reinforcement Learning Approach 257 Abolpour B Javan M and Karamouz M
Supervisory Control Strategy for a Rotary Kiln Process 311 Xiaojie Zhou Heng Yue and Tianyou Chai
TrialError Paradigm for Communications Network 325 Abdelhamid Mellouk
Application on Reinforcement
Learning for Diagnosis based on Medical Image 379 Stelmo Magalhaes Barros Netto Vanessa Rodrigues Coelho Leite
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Reinforcement Learning, second edition: An Introduction
Richard S. Sutton,Andrew G. Barto
Eingeschränkte Leseprobe - 1998
Abstraction action selection activity Adaptive AODV application approach Artificial Intelligence Barto basal ganglia behavior computation convergence cortex cost decision variables defined delay denotes dialogue dynamics environment equation error estimated evaluation example exploration genetic algorithm goal IEEE images initial input predictor variables interaction iteration kiln learning agent learning automata learning rate learning system Machine Learning Markov decision processes membership function MFLC module multi-agent neural network neurons node nodules OLSR optimization models output packet parameters path performance players prediction probabilistic probability problem proposed Q-learning Q-values quantum quantum computation qubit R-SSGA method random reinforcement learning reinforcement learning algorithms robot router routing RSGs rules sequence setpoint signal simple grammars simulation models space state-action pair step stochastic strategy sub-basin supervised learning Sutton Table task techniques temperature thermal comfort update vector