DocumentCode :
17124
Title :
; the agents have no prior information on the global state transition and on the local agent cost statistics. The network agents minimize a network-averaged infinite horizon discounted cost, by local processing and by collaborating through mutual information exchange over a sparse (possibly stochastic) communication network. The agents respond differently (depending on their instantaneous one-stage random costs) to a global controlled state and the control actions of a remote controller. When each agent is aware only of its local online cost data and the inter-agent communication network is weakly connected, we prove that <i>Q D</i>-learning, a consensus + innovations algorithm with mixed time-scale stochastic dynamics, converges asymptotically almost surely to the desired value function and to the optimal stationary control policy at each network agent.</div>
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<div class='leftDiv labelDiv col-xs-4 col-sm-2 fullRecLabelEnglish'>Keywords : </div><div class='valueDiv leftDirection leftAlign col-xs-8 col-sm-10 fullRecValueEnglish'>Markov processes; groupware; learning (artificial intelligence); minimisation; multi-agent systems; random processes; stochastic processes; telecontrol; MDP; QD-learning; collaborative distributed strategy; consensus-innovation algorithm; global controlled state; global state transition; instantaneous one-stage random costs; interagent communication network; local agent cost statistics; local online cost data; mixed time-scale stochastic dynamics; multiagent Markov decision process; mutual information exchange; network agents; network-averaged infinite horizon discounted cost; optimal stationary control policy; reinforcement Q -learning; remote controller; sparse communication network; stochastic communication network; Communication networks; Learning; Markov processes; Optimization; Process control; Symmetric matrices; Technological innovation; <formula formulatype=){kind=spacer}
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2013.2241057
Filename :
6415291
Link To Document :
