By Jian Fu, Haibo He, Qing Liu, Zhen Ni (auth.), Derong Liu, Huaguang Zhang, Marios Polycarpou, Cesare Alippi, Haibo He (eds.)
The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed complaints of the eighth foreign Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011.
The overall of 215 papers awarded in all 3 volumes have been conscientiously reviewed and chosen from 651 submissions. The contributions are established in topical sections on computational neuroscience and cognitive technological know-how; neurodynamics and intricate structures; balance and convergence research; neural community versions; supervised studying and unsupervised studying; kernel tools and help vector machines; blend versions and clustering; visible belief and trend reputation; movement, monitoring and item popularity; common scene research and speech reputation; neuromorphic undefined, fuzzy neural networks and robotics; multi-agent structures and adaptive dynamic programming; reinforcement studying and determination making; motion and motor keep watch over; adaptive and hybrid clever structures; neuroinformatics and bioinformatics; info retrieval; info mining and information discovery; and traditional language processing.
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Additional resources for Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part III
Keywords: reinforcement learning, adaptive critic design, dual heuristic programming, Delta-Bar-Delta, neural networks. 1 Introduction Dynamic Programming (DP)  is a general approach for sequential decision making under the framework of Markov decision processes (MDPs). However, classical dynamic programming algorithms, such as value iteration and policy iteration, are computationally expensive for MDPs with large or continuous state/action spaces. In recent years, approximate dynamic programming (ADP) and reinforcement learning (RL) have been widely studied to solve the difficulties in DP.
Let us assume that the path length is measured as the number of visited nodes, the robot starts at initial position and finishes at some collector node (in contrast to the previous model we will not need dummy destination). Let K = |W| be the number of waste nodes, L = |C| be the number of collector nodes, and C ≥ 1 be robot’s capacity. Then the maximal path length is 2K + 1 (collector node is visited immediately after a waste node). Computing the minimal path length is a bit tricky, we can assume the robot exploits fully its capacity - it visits exactly C waste nodes before going to a collector node (except the last loop).
The feasibility and the effectiveness of the proposed method are illustrated in the experiments on an Inverted Pendulum problem. Future work will include more theoretical and empirical analysis of adaptive learning rates in online reinforcement learning. Acknowledgments. This work is supported in part by the National Natural Science Foundation of China under Grant 60774076, 61075072 and 90820302, the Fork Ying Tung Education Foundation under Grant 114005, and the Natural Science Foundation of Hunan Province under Grant 2007JJ3122.
Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part III by Jian Fu, Haibo He, Qing Liu, Zhen Ni (auth.), Derong Liu, Huaguang Zhang, Marios Polycarpou, Cesare Alippi, Haibo He (eds.)