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» Tracking in Reinforcement Learning
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119
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CVPR
2010
IEEE
15 years 8 months ago
An Online Approach: Learning-Semantic-Scene-by-Tracking and Tracking-by-Learning-Semantic-Scene
Learning the knowledge of scene structure and tracking a large number of targets are both active topics of computer vision in recent years, which plays a crucial role in surveilla...
Xuan Song, Xiaowei Shao, Huijing Zhao, Jinshi Cui,...
HT
2009
ACM
15 years 10 months ago
Improving recommender systems with adaptive conversational strategies
Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes...
Tariq Mahmood, Francesco Ricci
100
Voted
ATAL
2007
Springer
15 years 9 months ago
Dynamic task allocation within an open service-oriented MAS architecture
A MAS architecture consisting of service centers is proposed. Within each service center, a mediator coordinates service delivery by allocating individual tasks to corresponding t...
Ivan Jureta, Stéphane Faulkner, Youssef Ach...
137
Voted
GECCO
2010
Springer
153views Optimization» more  GECCO 2010»
15 years 6 months ago
Multi-task evolutionary shaping without pre-specified representations
Shaping functions can be used in multi-task reinforcement learning (RL) to incorporate knowledge from previously experienced tasks to speed up learning on a new task. So far, rese...
Matthijs Snel, Shimon Whiteson
ATAL
2008
Springer
15 years 5 months ago
Sigma point policy iteration
In reinforcement learning, least-squares temporal difference methods (e.g., LSTD and LSPI) are effective, data-efficient techniques for policy evaluation and control with linear v...
Michael H. Bowling, Alborz Geramifard, David Winga...