Using the theory of regular functions to formally prove the ε-optimality of discretized pursuit learning algorithms

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

Volumn 8481, Issue , 2014, Pages 379-388

  Zhang, Xuan ^a   JohnOommen, B ^a,b   Granmo, Ole Christoffer ^a   Jiao, Lei ^a  

^a UNIVERSITY OF AGDER   (Norway)

^b CARLETON UNIVERSITY   (Canada)

Author keywords

Discretized Pursuit Algorithm; Pursuit Algorithms; optimality

Indexed keywords

AUTOMATA THEORY; INTELLIGENT SYSTEMS; REINFORCEMENT LEARNING;

ABSORBING STATE; ESTIMATOR ALGORITHMS; LEARNING AUTOMATON; LEARNING PARAMETERS; OPTIMALITY; PURSUIT ALGORITHMS; REGULAR FUNCTION;

LEARNING ALGORITHMS;

EID: 84958535496     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-319-07455-9_40     Document Type: Conference Paper

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