99 search results - page 10 / 20 » Action Selection in Bayesian Reinforcement Learning |
93
Voted
SARA
2007
Springer
15 years 3 months ago
2007
Springer
Several recent techniques for solving Markov decision processes use dynamic Bayesian networks to compactly represent tasks. The dynamic Bayesian network representation may not be g...
ICRA
2009
IEEE
15 years 4 months ago
2009
IEEE
— Our goal in this work is to make high level decisions for mobile robots. In particular, given a queue of prioritized object delivery tasks, we wish to find a sequence of actio...
103
Voted
ATAL
2005
Springer
15 years 3 months ago
2005
Springer
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
INLG
2010
Springer
14 years 7 months ago
2010
Springer
We present a novel approach to natural language generation (NLG) that applies hierarchical reinforcement learning to text generation in the wayfinding domain. Our approach aims to...
121
Voted
AI
1998
Springer
14 years 9 months ago
1998
Springer
Reinforcement Learning (RL) is the study of programs that improve their performance by receiving rewards and punishments from the environment. Most RL methods optimize the discoun...