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» Building Relational World Models for Reinforcement Learning
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ATAL
2007
Springer
14 years 1 months ago
Batch reinforcement learning in a complex domain
Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actio...
Shivaram Kalyanakrishnan, Peter Stone
WWW
2009
ACM
14 years 8 months ago
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
Community Question Answering (CQA) has emerged as a popular forum for users to pose questions for other users to answer. Over the last few years, CQA portals such as Naver and Yah...
Jiang Bian, Yandong Liu, Ding Zhou, Eugene Agichte...
ISCAS
2002
IEEE
153views Hardware» more  ISCAS 2002»
14 years 18 days ago
Biological learning modeled in an adaptive floating-gate system
We have implemented an aspect of learning and memory in the nervous system using analog electronics. Using a simple synaptic circuit we realize networks with Hebbian type adaptati...
Christal Gordon, Paul E. Hasler
KR
2004
Springer
14 years 1 months ago
Learning Probabilistic Relational Planning Rules
To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilist...
Hanna Pasula, Luke S. Zettlemoyer, Leslie Pack Kae...
IROS
2007
IEEE
136views Robotics» more  IROS 2007»
14 years 2 months ago
Affordance-based imitation learning in robots
— In this paper we build an imitation learning algorithm for a humanoid robot on top of a general world model provided by learned object affordances. We consider that the robot h...
Manuel Lopes, Francisco S. Melo, Luis Montesano