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ATAL
2005
Springer
15 years 8 months ago
Improving reinforcement learning function approximators via neuroevolution
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Shimon Whiteson
ROMAN
2007
IEEE
134views Robotics» more  ROMAN 2007»
15 years 9 months ago
Learning Reward Modalities for Human-Robot-Interaction in a Cooperative Training Task
—This paper proposes a novel method of learning a users preferred reward modalities for human-robot interaction through solving a cooperative training task. A learning algorithm ...
Anja Austermann, Seiji Yamada
HRI
2011
ACM
14 years 6 months ago
A robotic game to evaluate interfaces used to show and teach visual objects to a robot in real world condition
In this paper, we present a real world user study of 4 interfaces designed to teach new visual objects to a social robot. This study was designed as a robotic game in order to mai...
Pierre Rouanet, Fabien Danieau, Pierre-Yves Oudeye...
ATAL
2009
Springer
15 years 9 months ago
An empirical analysis of value function-based and policy search reinforcement learning
In several agent-oriented scenarios in the real world, an autonomous agent that is situated in an unknown environment must learn through a process of trial and error to take actio...
Shivaram Kalyanakrishnan, Peter Stone
ATAL
2007
Springer
15 years 9 months ago
IFSA: incremental feature-set augmentation for reinforcement learning tasks
Reinforcement learning is a popular and successful framework for many agent-related problems because only limited environmental feedback is necessary for learning. While many algo...
Mazda Ahmadi, Matthew E. Taylor, Peter Stone