Journal
NEUROCOMPUTING
Volume 156, Issue -, Pages 157-165Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2014.12.067
Keywords
Multiple-model; Optimal control; Adaptive self-organizing map; Reinforcement learning; Value function approximation
Categories
Funding
- NSF [ECCS-1405173, IIS-1208623]
- ONR [N00014-13-1-0562, N000141410718]
- ARO [W911NF-11-D-0001]
- China NNSF grant [61120106011]
- China Education Ministry Project 111 [B08015]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1405173, 1128050] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1208623] Funding Source: National Science Foundation
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In this paper motivated by recently discovered neurocognitive models of mechanisms in the brain, a new reinforcement learning (RL) method is presented based on a novel critic neural network (NN) structure to solve the optimal tracking problem of a nonlinear discrete time-varying system in an online manner. A multiple-model approach combined with an adaptive self-organizing map (ASOM) neural network is used to detect changes in the dynamics of the system. The number of sub-models is determined adaptively and grows once a mismatch between the stored sub-models and the new data is detected. By using the ASOM neural network, a novel value function approximation (VFA) scheme is presented. Each sub-model contributes into the value function based on a responsibility signal obtained by the ASOM. The responsibility signal determines how much each sub-model contributes to the general value function. Novel policy iteration and the value iteration algorithms are presented to find the optimal control for the partially-unknown nonlinear discrete time-varying systems in an online manner. Simulation results demonstrate the effectiveness of the proposed control scheme. (C) 2014 Elsevier B.V. All rights reserved.
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