MEC-A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems

In this paper, the first probably approximately correct (PAC) algorithm for continuous deterministic systems without relying on any system dynamics is proposed. It combines the state aggregation technique and the efficient exploration principle, and makes high utilization of online observed samples....

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 26; číslo 2; s. 346 - 356
Hlavní autori:	Zhao, Dongbin, Zhu, Yuanheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.02.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithm design and analysis Algorithms Approximation algorithms Computer simulation Dynamical systems Dynamics Efficient exploration Exploration Heuristic algorithms Learning Learning systems Mathematical analysis Neural networks Partitioning algorithms Policies Polynomials probably approximately correct (PAC) reinforcement learning (RL) state aggregation Upper bound Efficient exploration probably approximately correct (PAC) state aggregation reinforcement learning (RL)
ISSN:	2162-237X, 2162-2388, 2162-2388
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