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ICML
2007
IEEE
14 years 8 months ago
Conditional random fields for multi-agent reinforcement learning
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...
ALT
2006
Springer
14 years 4 months ago
Probabilistic Generalization of Simple Grammars and Its Application to Reinforcement Learning
Abstract. Recently, some non-regular subclasses of context-free grammars have been found to be efficiently learnable from positive data. In order to use these efficient algorithms ...
Takeshi Shibata, Ryo Yoshinaka, Takashi Chikayama
IROS
2007
IEEE
132views Robotics» more  IROS 2007»
14 years 2 months ago
Hysteretic q-learning : an algorithm for decentralized reinforcement learning in cooperative multi-agent teams
— Multi-agent systems (MAS) are a field of study of growing interest in a variety of domains such as robotics or distributed controls. The article focuses on decentralized reinf...
Laëtitia Matignon, Guillaume J. Laurent, Nadi...
P2P
2006
IEEE
101views Communications» more  P2P 2006»
14 years 1 months ago
Reinforcement Learning for Query-Oriented Routing Indices in Unstructured Peer-to-Peer Networks
The idea of building query-oriented routing indices has changed the way of improving routing efficiency from the basis as it can learn the content distribution during the query r...
Cong Shi, Shicong Meng, Yuanjie Liu, Dingyi Han, Y...
ICML
2003
IEEE
14 years 1 months ago
The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping
Shaping can be an effective method for improving the learning rate in reinforcement systems. Previously, shaping has been heuristically motivated and implemented. We provide a for...
Adam Laud, Gerald DeJong