67 search results - page 5 / 14 » Learning predictive state representations using non-blind po... |
NIPS
2001
13 years 9 months ago
2001
We address two open theoretical questions in Policy Gradient Reinforcement Learning. The first concerns the efficacy of using function approximation to represent the state action ...
CORR
2010
Springer
13 years 6 months ago
2010
Springer
We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications...
ICML
2010
IEEE
13 years 9 months ago
2010
IEEE
We present the first temporal-difference learning algorithm for off-policy control with unrestricted linear function approximation whose per-time-step complexity is linear in the ...
ICRA
2005
IEEE
14 years 1 months ago
2005
IEEE
— We present a semi-parametric control policy representation and use it to solve a series of nonholonomic control problems with input state spaces of up to 7 dimensions. A neares...
NN
2007
Springer
13 years 7 months ago
2007
Springer
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...