"A Pulse-Based Reinforcement Algorithm for Learning Continuous Functions"

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Book	Author(s)	Year	Publisher
Reinforcement Learning for Optimal Feedback Control: A Lyapunov-Based Approach	Rushikesh Kamalapurkar; Patrick Walters; Joel Rosenfeld; Warren Dixon	1st ed. 2018	Springer International Publishing
Algorithms for Reinforcement Learning	Csaba Szepesvári		Springer International Publishing
Algorithms for reinforcement learning	Csaba Szepesvári	2010	Morgan & Claypool
Continuous Functions	Jacques Simon	2020	John Wiley & Sons
Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles	Li Yeuching; He Hongwen		Springer International Publishing
Reinforcement Learning	Richard S. Sutton	1992	Springer US
Reinforcement Learning	Sutton, Barto	2018
Adaptive Representations for Reinforcement Learning	Shimon Whiteson	2011	Springer Berlin Heidelberg
Deep Reinforcement Learning with Guaranteed Performance: A Lyapunov-Based Approach	Yinyan Zhang; Shuai Li; Xuefeng Zhou	1st ed. 2020	Springer International Publishing
Transfer Learning for Multiagent Reinforcement Learning Systems	Felipe Leno Silva; Anna Helena Reali Costa		Springer International Publishing
Escape learning and "vicious circle" behavior under partial and continuous reinforcement.	Melvin, Kenneth Boyd, Jr.	1963	University of Florida
Deep Reinforcement Learning	Aske Plaat	1st ed. 2022	Springer Nature Singapore
Learning and Reinforcement	Stephen F. Walker	1975	Methuen
Practical Reinforcement Learning	Farrukh Akhtar	2017	Packt Publishing
Learning: reinforcement theory	Fred Simmons Keller
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning	Abhijit Gosavi	2003	Springer US
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning	Abhijit Gosavi	2nd ed. 2015	Springer US
Design of Experiments for Reinforcement Learning	Christopher Gatti	2015	Springer International Publishing
Deep Reinforcement Learning for Wireless Networks	F. Richard Yu; Ying He	1st ed. 2019	Springer International Publishing
Teaching Learning Based Optimization Algorithm: And Its Engineering Applications	R. Venkata Rao	1st ed. 2016	Springer International Publishing
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
A Pulse-Based Reinforcement Algorithm for Learning Continuous Functions	D Gorse; J G Taylor; T G Clarkson	World Congress On Neural Networks-San Diego: 1994 International Neural Network Society Annual Meeting	Paul Werbos; Harold Szu; Bernard Widrow	1994	Routledge
Bacterial Foraging Algorithm Based on Reinforcement Learning for Continuous Optimizations	Huiyan Jiang; Wanpeng Dong; Lianbo Ma; Rui Wang	Computational Intelligence and Intelligent Systems: 9th International Symposium, ISICA 2017, Guangzhou, China, November 18–19, 2017, Revised Selected Papers, Part I	Kangshun Li; Wei Li; Zhangxing Chen; Yong Liu	1st ed. 2018	Springer Nature Singapore
Learning Graph-Based Representations for Continuous Reinforcement Learning Domains	Jan Hendrik Metzen	Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I	Hendrik Blockeel; Kristian Kersting; Siegfried Nijssen; Filip Železný	2013	Springer Berlin Heidelberg
A reinforcement learning algorithm based on ‘safety’	A. Nowé; R. Vepa	Fuzzy Logic in Artificial Intelligence: 8th Austrian Artificial Intelligence Conference, FLAI' 93, Linz, Austria, June 28-30, 1993. Proceedings	Erich P. Klement; Wolfgang Slany	1993	Springer Berlin Heidelberg
Reinforcement learning algorithm		HANDS-ON REINFORCEMENT LEARNING WITH PYTHON -: master reinforcement and deep reinforcement. . . learning from scratch using openai gym and tensorf.	SUDHARSAN RAVICHANDIRAN	2020	PACKT PUBLISHING LIMITED
A Reinforcement Learning Based Algorithm for Robot Action Planning	Marko Švaco; Bojan Jerbić; Mateo Polančec; Filip Šuligoj	Advances in Service and Industrial Robotics: Proceedings of the 27th International Conference on Robotics in Alpe-Adria Danube Region (RAAD 2018)	Nikos A. Aspragathos; Panagiotis N. Koustoumpardis; Vassilis C. Moulianitis	1st ed. 2019	Springer International Publishing
A Novel Optimization Algorithm Based on Reinforcement Learning	Janusz A. Starzyk; Yinyin Liu; Sebastian Batog	Computational Intelligence in Optimization: Applications and Implementations	Yoel Tenne; Chi-Keong Goh	2010	Springer Berlin Heidelberg
68 Reinforcement Learning Algorithm		Machine learning algorithms using Python programming	Gaurav Patil; Gopal Sakarkar; Prateek Dutta	2021	Nova Science Publishers
Continuous Reinforcement Learning Algorithm for Skills Learning in an Autonomous Mobile Robot	Ma Jesús López Boada; Ramón Barber; Verónica Egido; Miguel Ángel Salichs	Machine Learning and Robot Perception	Bruno Apolloni; Ashish Ghosh; Ferda Alpaslan; Lakhmi Jain; Srikanta Patnaik	2005	Springer Berlin Heidelberg
Continuous Reinforcement Learning Algorithm for Skills Learning in an Autonomous Mobile Rob		Machine Learning and Robot Perception	Mario Mata, Jose Maria Armingol (auth.), Professor Bruno Apolloni, Professor Ashish Ghosh, Professor Ferda Alpaslan, Professor Lakhmi C. Jain, Professor Srikanta Patnaik (eds.)	2005	Springer-Verlag Berlin Heidelberg
Gobang Game Algorithm Based on Reinforcement Learning	Xiali Li; Wei Zhang; Junren Chen; Licheng Wu; Cairangdanghzhou	Cognitive Systems and Information Processing: 6th International Conference, ICCSIP 2021, Suzhou, China, November 20–21, 2021, Revised Selected Papers	Fuchun Sun; Dewen Hu; Stefan Wermter; Lei Yang; Huaping Liu; Bin Fang	1st ed. 2022	Springer Nature Singapore
Fuzzy RANSAC Algorithm based on Reinforcement Learning		Fuzzy Modeling and Control. Methods, Applications and Research	Terrell Harvey, Dallas Mullins (eds.)	2018	Nova
Reinforcement Learning Reward Functions for Unsupervised Learning	Colin Fyfe; Pei Ling Lai	Advances in Neural Networks - ISNN 2007: 4th International Symposium on Neutral Networks, ISNN 2007 Nanjing, China, June 3-7, 2007. Proceedings, Part I	Derong Liu; Shumin Fei; Zeng-Guang Hou; Huaguang Zhang; Changyin Sun	2007	Springer Berlin Heidelberg
A Reinforcement Learning Based Hybrid Evolutionary Algorithm for Ship Stability Design	Osman Turan; Hao Cui	Variants of Evolutionary Algorithms for Real-World Applications	Raymond Chiong; Thomas Weise; Zbigniew Michalewicz	2012	Springer Berlin Heidelberg
A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance	Jeremy Roghair; Amir Niaraki; Kyungtae Ko; Ali Jannesari	Intelligent Systems and Applications: Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 1	Kohei Arai	1st ed. 2022	Springer International Publishing
A Modular Hierarchical Reinforcement Learning Algorithm	Zhibin Liu; Xiaoqin Zeng; Huiyi Liu	Intelligent Computing Theories and Applications: 8th International Conference, ICIC 2012, Huangshan, China, July 25-29, 2012, Proceedings	De-Shuang Huang; Jianhua Ma; Kang-Hyun Jo; M. Michael Gromiha	2012	Springer Berlin Heidelberg
A Multi-objective Reinforcement Learning Algorithm for JSSP	Beatriz M. Méndez-Hernández; Erick D. Rodríguez-Bazan; Yailen Martinez-Jimenez; Pieter Libin; Ann Nowé	Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I	Igor V. Tetko; Věra Kůrková; Pavel Karpov; Fabian Theis	1st ed. 2019	Springer International Publishing
Reinforcement Learning Algorithm with CTRNN in Continuous Action Space	Hiroaki Arie; Jun Namikawa; Tetsuya Ogata; Jun Tani; Shigeki Sugano	Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part I	Jun Wang; Laiwan Chan; DeLiang Wang	2006	Springer Berlin Heidelberg
Reinforcement Learning Algorithm with CTRNN in Continuous Action Spa		Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006. Proceedings, Part I	Colin Molter, Naoyuki Sato, Utku Salihoglu, Yoko Yamaguchi (auth.), Irwin King, Jun Wang, Lai-Wan Chan, DeLiang Wang (eds.)	2006	Springer-Verlag Berlin Heidelberg
Continuous-Time Spike-Based Reinforcement Learning for Working Memory Tasks	Marios Karamanis; Davide Zambrano; Sander Bohté	Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part II	Věra Kůrková; Yannis Manolopoulos; Barbara Hammer; Lazaros Iliadis; Ilias Maglogiannis	1st ed. 2018	Springer International Publishing
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