109 search results - page 15 / 22 » Policy teaching through reward function learning |
ECML
2004
Springer
14 years 1 months ago
2004
Springer
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
ICRA
2007
IEEE
14 years 1 months ago
2007
IEEE
— While the Partially Observable Markov Decision Process (POMDP) provides a formal framework for the problem of robot control under uncertainty, it typically assumes a known and ...
KCAP
2009
ACM
14 years 2 months ago
2009
ACM
As computational learning agents move into domains that incur real costs (e.g., autonomous driving or financial investment), it will be necessary to learn good policies without n...
CORR
2008
Springer
13 years 7 months ago
2008
Springer
We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperati...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
This paper formalizes Feature Selection as a Reinforcement Learning problem, leading to a provably optimal though intractable selection policy. As a second contribution, this pape...