Iteratively extending time horizon reinforcement learning

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2837, Issue , 2003, Pages 96-107

  Ernst, Damien ^a   Geurts, Pierre ^a   Wehenkel, Louis ^a  

^a UNIVERSITY OF LIÈGE   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; CLOSED LOOP CONTROL SYSTEMS; CONVERGENCE OF NUMERICAL METHODS; FUNCTIONS; ITERATIVE METHODS; OPTIMAL CONTROL SYSTEMS; PROBLEM SOLVING; RANDOM PROCESSES; STANDARDS; TIME DOMAIN ANALYSIS; VECTORS; APPROXIMATION ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; REINFORCEMENT LEARNING; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC SYSTEMS; SUPERVISED LEARNING;

OPTIMAL CONTROL POLICY; REINFORCEMENT LEARNING; REWARD FUNCTIONS; TIME HORIZONS; CONTROL PROBLEMS; INFINITE TIME HORIZON; OPTIMAL CONTROLS; OPTIMALITY CRITERIA; REGRESSION TREES; STOCHASTIC APPROXIMATIONS; SUPERVISED LEARNING PROBLEMS;

LEARNING SYSTEMS; LEARNING ALGORITHMS;

EID: 9444250519     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-39857-8_11     Document Type: Conference Paper

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