Ensemble algorithms in reinforcement learning

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 38, Issue 4, 2008, Pages 930-936

  Wiering, Marco A ^a   van Hasselt, Hado ^b  

^a UNIVERSITY OF GRONINGEN   (Netherlands)

^b UTRECHT UNIVERSITY   (Netherlands)

Author keywords

Dynamic mazes; Ensemble algorithms; Partially observable environments; Reinforcement learning (RL)

Indexed keywords

ALGORITHMS; CHLORINE COMPOUNDS; EDUCATION; LEARNING SYSTEMS; MATHEMATICAL PROGRAMMING; PROBABILITY; REINFORCEMENT; REINFORCEMENT LEARNING; SYSTEMS ENGINEERING;

ACTOR-CRITIC; BOLTZMANN; DYNAMIC MAZES; ENHANCE LEARNING; ENSEMB LE METHODS; ENSEMBLE ALGORITHMS; LEARNING AUTOMATON; MAJORITY VOTING; MAZE PROBLEMS; PARTIALLY OBSERVABLE ENVIRONMENTS; Q-LEARNING; REINFORCEMENT LEARNING (RL); SINGLE AGENTS; SINGLE-VALUE; VALUE FUNCTIONS;

LEARNING ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; FEEDBACK SYSTEM; REINFORCEMENT; SYSTEM ANALYSIS; SYSTEMS THEORY; THEORETICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; FEEDBACK; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); PROGRAMMING, LINEAR; REINFORCEMENT (PSYCHOLOGY); SYSTEMS THEORY;

EID: 49049105169     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2008.920231     Document Type: Article

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