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CORR
2010
Springer
204views Education» more  CORR 2010»
13 years 6 months ago
Predictive State Temporal Difference Learning
We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications...
Byron Boots, Geoffrey J. Gordon
ACSC
2008
IEEE
13 years 9 months ago
An investigation of the state formation and transition limitations for prediction problems in recurrent neural networks
Recurrent neural networks are able to store information about previous as well as current inputs. This "memory" allows them to solve temporal problems such as language r...
Angel Kennedy, Cara MacNish
ICML
2008
IEEE
14 years 8 months ago
On-line discovery of temporal-difference networks
We present an algorithm for on-line, incremental discovery of temporal-difference (TD) networks. The key contribution is the establishment of three criteria to expand a node in TD...
Takaki Makino, Toshihisa Takagi
ATAL
2008
Springer
13 years 9 months ago
Dynamics based control with PSRs
We present an extension of the Dynamics Based Control (DBC) paradigm to environment models based on Predictive State Representations (PSRs). We show an approximate greedy version ...
Ariel Adam, Zinovi Rabinovich, Jeffrey S. Rosensch...
NN
2007
Springer
162views Neural Networks» more  NN 2007»
13 years 7 months ago
Learning grammatical structure with Echo State Networks
Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. Howeve...
Matthew H. Tong, Adam D. Bickett, Eric M. Christia...