TY - CHAP A1 - Yasushi Kobayashi A2 - Ken-ichi Okada ED1 - Abdelhamid Mellouk Y1 - 2011-01-14 PY - 2011 T1 - Reward Prediction Error Computation in the Pedunculopontine Tegmental Nucleus Neurons N2 - Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic. BT - Advances in Reinforcement Learning SP - Ch. 9 UR - https://doi.org/10.5772/13378 DO - 10.5772/13378 SN - PB - IntechOpen CY - Rijeka Y2 - 2021-06-15 ER -