A tutorial on linear function approximators for dynamic programming and reinforcement learning

This tutorial reviews techniques for planning and learning in Markov Decision Processes (MDPs) with linear function approximation of the value function. Two major paradigms for finding optimal policies were considered: dynamic programming (DP) techniques for planning and reinforcement learning (RL).

Detalles Bibliográficos
Autores principales: Geramifard, Alborz, Walsh, Thomas J, Stefanie, Tellex, Chowdhary, Girish, Roy, Nicholas, How, Jonathan P
Lenguaje:eng
Publicado: Now Publishers 2013
Materias:
XX
Acceso en línea:http://cds.cern.ch/record/2762208

Internet

http://cds.cern.ch/record/2762208

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