210 search results - page 2 / 42 » An analysis of reinforcement learning with function approxim... |
107
Voted
ICML
2007
IEEE
16 years 1 months ago
2007
IEEE
Reinforcement learning algorithms can become unstable when combined with linear function approximation. Algorithms that minimize the mean-square Bellman error are guaranteed to co...
97
Voted
ICML
2009
IEEE
16 years 1 months ago
2009
IEEE
98
Voted
ATAL
2009
Springer
15 years 6 months ago
2009
Springer
In several agent-oriented scenarios in the real world, an autonomous agent that is situated in an unknown environment must learn through a process of trial and error to take actio...
90
Voted
NIPS
2001
15 years 1 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 ...
116
Voted
NN
2010
Springer
14 years 7 months ago
2010
Springer
Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares ...