139 search results - page 7 / 28 » Model-based function approximation in reinforcement learning |
59
Voted
ICML
2007
IEEE
15 years 10 months ago
2007
IEEE
This paper investigates the problem of automatically learning how to restructure the reward function of a Markov decision process so as to speed up reinforcement learning. We begi...
IAT
2003
IEEE
15 years 2 months ago
2003
IEEE
A gradient-based method for both symmetric and asymmetric multiagent reinforcement learning is introduced in this paper. Symmetric multiagent reinforcement learning addresses the ...
80
Voted
ICRA
2009
IEEE
15 years 4 months ago
2009
IEEE
Abstract— Least-squares policy iteration is a useful reinforcement learning method in robotics due to its computational efficiency. However, it tends to be sensitive to outliers...
90
Voted
ESANN
2008
14 years 11 months ago
2008
Using multilayer perceptrons (MLPs) to approximate the state-action value function in reinforcement learning (RL) algorithms could become a nightmare due to the constant possibilit...
ICML
2001
IEEE
15 years 10 months ago
2001
IEEE
We introduce the first algorithm for off-policy temporal-difference learning that is stable with linear function approximation. Off-policy learning is of interest because it forms...