TY - CHAP A1 - Minoru Tsukada ED1 - Cornelius Weber ED2 - Mark Elshaw ED3 - Norbert Michael Mayer Y1 - 2008-01-01 PY - 2008 T1 - Interaction Between the Spatio-Temporal Learning Rule (Non Hebbian) and Hebbian in Single Cells: A Cellular Mechanism of Reinforcement Learning N2 - 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. BT - Reinforcement Learning SP - Ch. 6 UR - https://doi.org/10.5772/5277 DO - 10.5772/5277 SN - PB - IntechOpen CY - Rijeka Y2 - 2019-11-14 ER -