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» Policy teaching through reward function learning
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SIGECOM
2009
ACM
114views ECommerce» more  SIGECOM 2009»
9 years 7 months ago
Policy teaching through reward function learning
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, ...
Haoqi Zhang, David C. Parkes, Yiling Chen
NN
2010
Springer
187views Neural Networks» more  NN 2010»
8 years 8 months ago
Efficient exploration through active learning for value function approximation in reinforcement learning
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 ...
Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiya...
JMLR
2010
189views more  JMLR 2010»
8 years 8 months ago
Adaptive Step-size Policy Gradients with Average Reward Metric
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 ...
Takamitsu Matsubara, Tetsuro Morimura, Jun Morimot...
ICRA
2008
IEEE
128views Robotics» more  ICRA 2008»
9 years 7 months ago
Intrinsically motivated hierarchical manipulation
— 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...
Stephen Hart, Shijaj Sen, Roderic A. Grupen
ATAL
2010
Springer
9 years 2 months ago
Combining manual feedback with subsequent MDP reward signals for reinforcement learning
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...
W. Bradley Knox, Peter Stone
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