36 search results - page 1 / 8 » Posterior Weighted Reinforcement Learning with State Uncerta... |
NECO
2010
13 years 6 months ago
2010
Reinforcement learning models generally assume that a stimulus is presented that allows a learner to unambiguously identify the state of nature, and the reward received is drawn f...
NIPS
1996
13 years 9 months ago
1996
Model learning combined with dynamic programming has been shown to be e ective for learning control of continuous state dynamic systems. The simplest method assumes the learned mod...
AAAI
2012
11 years 10 months ago
2012
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
ICRA
2008
IEEE
14 years 2 months ago
2008
IEEE
— We consider the problem of optimal control in continuous and partially observable environments when the parameters of the model are not known exactly. Partially Observable Mark...
PKDD
2009
Springer
14 years 2 months ago
2009
Springer
Abstract. The Factored Markov Decision Process (FMDP) framework is a standard representation for sequential decision problems under uncertainty where the state is represented as a ...