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Morimoto J, Doya K (2007) Reinforcement learning state estimator. Neural Comput 19:730-56 [PubMed]

References and models cited by this paper

References and models that cite this paper

Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear-non-gaussian bayesian tracking IEEE Trans Signal Process 50:174-188

Baird LC, Moore AW (1999) Gradient descent for general reinforcement learning Advances in neural information processing systems, Kearns MS:Solla S:Dohn D, ed.

Barto AG, Sutton RS, Anderson CW (1983) Neuronlike elements that can solve difficult learning control problems IEEE Trans Systems Man Cybern 13:835-846

Crassidis JL, Junkins JL (2004) Optimal estimation of dynamic systems

Doucet A, Godsill S, Andrieu C (2000) On sequential Monte Carlo sampling methods for Bayesian filtering Stat Comput 10:197-208

Doya K (2000) Reinforcement learning in continuous time and space. Neural Comput 12:219-45 [PubMed]

Erdogmus D, Genc AU, Principe JC (2002) A neural network perspectiveto extended Luenberger observers Institute Of Measurement And Control 35:10-16

Goswami A, Thuilot B, Espiau B (1996) Compass-like biped robot part I: Stability and bifurcation of passive gaits Tech Rep No RR-2996 INRI

Jaakkola T, Singh SP, Jordan MI (1995) Reinforcement learning algorithm for partially observable Markov decision problems Advances in neural information processing systems, Tesauro G:Touretzky D:Leen T, ed. pp.345

Kalman RE, Bucy R (1961) New results in linear filtering and prediction Trans ASME J Basic Eng 83:95-108

Kimura H, Kobayashi S (1998) An analysis of actor-critic algorithms using eligibility traces: Reinforcement learning with imperfect value functions Proc 15th Intl Conf Mach Learn :284-292

Littman ML, Cassandra AR, Kaelbling LP (1995) Learning policies for partially observable environments: Scaling up Proc 12th Intl Conf Mach Learn :363-370

Luenberger DG (1971) An introduction to observers IEEE Trans 16:596-602

McCallum RA (1995) Reinforcement learning with selective perception and hidden state Unpublished doctoral dissertation, University of Rochester

Meuleau N, Kim KE, Kaelbling LP (2001) Exploration in gradient-based reinforcement learning Tech Rep MIT

Meuleau N, Peshkin L, Kim KE, Kaelbling LP (2000) Learning finite state controllers for partially observable environments Proc 15th Ann Conf Uncertainty in Artificial Intelligence :427-436

Morimoto J, Doya K (2002) Development of an observer by using reinforcement learning Proc 12th Annual Conf Japn Neural Network Soc :275-278

Porter LL, Passino KM (1995) Genetic adaptive observers Engineering Applications of Artificial Intelligence 8:261-269

Raghavan IR, Hedrick JK (1994) Observer design for a class of nonlinear systems International Journal Of Control 59:515-528

Sutton RS, Barto AG (1998) Reinforcement learning: an introduction [Journal]

   A reinforcement learning example (Sutton and Barto 1998) [Model]

Thau FE (1973) Observing the state of nonlinear dynamic systems Intl J Control 17:471-479

Thrun S (2000) Monte Carlo POMDPs Advances in neural information processing systems, Solla SA:Leen TK:Muller KR, ed. pp.1064

Wan E, van_der_Merwe R (2000) The unscented Kalman filter for nonlinear estimation Proc IEEE Symposium

Wan EA, Nelson AT (1997) Dual Kalman filtering methods for nonlinear prediction, smoothing, and estimation Advances in neural information processing systems, Mozer M:Jordan M:Petsche T, ed. pp.793

Xu JW, Erdogmus D, Principe JC (2005) Minimum error entropy Luenberger observer Proc Am Control Conf

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