85 search results - page 14 / 17 » Solving Stochastic Planning Problems with Large State and Ac... |
CG
1998
Springer
13 years 11 months ago
1998
Springer
Humans can e ectively navigate through large search spaces, enabling them to solve problems with daunting complexity. This is largely due to an ability to successfully distinguish ...
UAI
2008
13 years 8 months ago
2008
We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available afte...
AIPS
2006
13 years 8 months ago
2006
We present a new algorithm for conformant probabilistic planning, which for a given horizon produces a plan that maximizes the probability of success under quantified uncertainty ...
ISRR
2005
Springer
14 years 29 days ago
2005
Springer
ys when planning meant searching for a sequence of abstract actions that satisfied some symbolic predicate. Robots can now learn their own representations through statistical infe...
AIIA
2007
Springer
14 years 1 months ago
2007
Springer
The application of Reinforcement Learning (RL) algorithms to learn tasks for robots is often limited by the large dimension of the state space, which may make prohibitive its appli...