Extended replicator dynamics as a key to reinforcement learning in multi-agent systems

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

Volumn 2837, Issue , 2003, Pages 421-431

  Tuyls, Karl ^a   Heytens, Dries ^a   Nowe, Ann ^a   Manderick, Bernard ^a  

^a VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; AUTOMATA THEORY; COMPUTATIONAL METHODS; CONVERGENCE OF NUMERICAL METHODS; DIFFERENTIAL EQUATIONS; GAME THEORY; GENES; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MUTAGENESIS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; DYNAMICS; EVOLUTIONARY ALGORITHMS; INTELLIGENT AGENTS; MULTI AGENT SYSTEMS; REINFORCEMENT LEARNING; SOFTWARE AGENTS; TELECOMMUNICATION NETWORKS;

LEARNING AGENTS; MULTI-STATE GAMES; REINFORCEMENT LEARNING (RL) TECHNIQUES; REPLICATOR DYNAMICS (RD); DYNAMIC BEHAVIOURS; EVOLUTIONARY GAME THEORY; LEARNING PROCESS; MODEL LEARNING; MULTI-STATE; NASH EQUILIBRIA; REINFORCEMENT LEARNING TECHNIQUES; REPLICATOR DYNAMICS;

MULTI AGENT SYSTEMS; LEARNING ALGORITHMS;

EID: 9444229990     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-39857-8_38     Document Type: Conference Paper

References (13)

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