109 search results - page 1 / 22 » Policy teaching through reward function learning |
SIGECOM
2009
ACM
13 years 10 months ago
2009
ACM
Policy teaching considers a Markov Decision Process setting in which an interested party aims to influence an agent’s decisions by providing limited incentives. In this paper, ...
NN
2010
Springer
12 years 11 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 ...
JMLR
2010
12 years 11 months ago
2010
In this paper, we propose a novel adaptive step-size approach for policy gradient reinforcement learning. A new metric is defined for policy gradients that measures the effect of ...
ICRA
2008
IEEE
13 years 10 months ago
2008
IEEE
— We present a framework for the programming of manipulation behavior by means of an intrinsic reward function that encourages the building of deep control knowledge. We show how...
ATAL
2010
Springer
13 years 5 months ago
2010
Springer
As learning agents move from research labs to the real world, it is increasingly important that human users, including those without programming skills, be able to teach agents de...