Presentation 2011-06-24
Solving POMDPs using Restricted Boltzmann Machines with Echo State Networks
Makoto OTSUKA, Junichiro YOSHIMOTO, Stefan ELFWING, Kenji DOYA,
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Abstract(in English) A partially observable Markov decision process (POMDP) can be solved in a model-based way using explicit knowledge of the environmental dynamics or in a model-free way using implicit representations of task-relevant states. Here we consider a model-free approach of combining an echo state network (ESN) for summarizing past actions and observations and a restricted Boltzmann machine (RBM) for learning action values in a high-dimensional state space. Simulation results in robot navigation tasks showed that the ESN can capture relevant information in the sequence of high dimensional observations and that RBM can construct task-oriented internal representation in its hidden layer.
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Keyword(in English) partially observable Markov decision processes / restricted Boltzmann machines / echo state networks / goal-directed representation / free energy
Paper # NC2011-19
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Committee NC
Conference Date 2011/6/16(1days)
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Registration To Neurocomputing (NC)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Solving POMDPs using Restricted Boltzmann Machines with Echo State Networks
Sub Title (in English)
Keyword(1) partially observable Markov decision processes
Keyword(2) restricted Boltzmann machines
Keyword(3) echo state networks
Keyword(4) goal-directed representation
Keyword(5) free energy
1st Author's Name Makoto OTSUKA
1st Author's Affiliation Neural Computation Unit, Okinawa Institute of Science and Technology()
2nd Author's Name Junichiro YOSHIMOTO
2nd Author's Affiliation Neural Computation Unit, Okinawa Institute of Science and Technology
3rd Author's Name Stefan ELFWING
3rd Author's Affiliation Neural Computation Unit, Okinawa Institute of Science and Technology
4th Author's Name Kenji DOYA
4th Author's Affiliation Neural Computation Unit, Okinawa Institute of Science and Technology
Date 2011-06-24
Paper # NC2011-19
Volume (vol) vol.111
Number (no) 96
Page pp.pp.-
#Pages 6
Date of Issue