Estimating internal variables and parameters of a learning agent by a particle filter

Advances in Neural Information Processing Systems

Volumn , Issue , 2004, Pages

  Samejima, Kazuyuki ^a   Doya, Kenji ^a   Ueda, Yasumasa ^b   Kimura, Minoru ^b  

^a ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

^b KYOTO PREFECTURAL UNIVERSITY OF MEDICINE   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; ESTIMATION; MONTE CARLO METHODS; REINFORCEMENT LEARNING;

BAYESIAN ESTIMATIONS; CONNECTION WEIGHTS; HIGHER ORDER FUNCTIONS; INTERNAL PARAMETERS; LEARNING AND MEMORY; OBSERVABLE VARIABLES; POSTERIOR PROBABILITY; REINFORCEMENT LEARNING MODELS;

PARAMETER ESTIMATION;

EID: 68949161906     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. Sutton RS & Barto AG (1998) Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA.
2. Schultz W, Dayan P, Montague PR (1997) A neural substrate of prediction and reward. Science. 14;275(5306):1593-1599.
3. Doucet A, de Freitas N and Gordon N, (2001) An introduction to sequential Monte Carlo methods, In Sequential Monte Carlo Methods in Practice, Doucet A, de Freitas N & Gordon N eds, Springer-Verlag, pp.3-14.
4. Ueda Y, Samejima K, Doya K, & Kimura M (2002) Reward value dependent striate neuron activity of monkey performing trial-and-error behavioral decision task, Abst. of Soc Neurosci, 765.13.
5. O'Doherty, Dayan P, Friston K, Critchley H and Dolan R (2003) Temporal difference models and reward-related learning in human brain, Neuron 28, 329-337.