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...
—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 ...
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...
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...
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...