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» The Dynamics of Multi-Agent Reinforcement Learning
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AR
2008
118views more  AR 2008»
14 years 9 months ago
Efficient Behavior Learning Based on State Value Estimation of Self and Others
The existing reinforcement learning methods have been seriously suffering from the curse of dimension problem especially when they are applied to multiagent dynamic environments. ...
Yasutake Takahashi, Kentarou Noma, Minoru Asada
NIPS
1996
14 years 11 months ago
Why did TD-Gammon Work?
Although TD-Gammon is one of the major successes in machine learning, it has not led to similar impressive breakthroughs in temporal difference learning for other applications or ...
Jordan B. Pollack, Alan D. Blair
IROS
2008
IEEE
165views Robotics» more  IROS 2008»
15 years 4 months ago
Mutual development of behavior acquisition and recognition based on value system
Abstract. Both self-learning architecture (embedded structure) and explicit/implicit teaching from other agents (environmental design issue) are necessary not only for one behavior...
Yasutake Takahashi, Yoshihiro Tamura, Minoru Asada
CIG
2005
IEEE
15 years 3 months ago
Nannon: A Nano Backgammon for Machine Learning Research
A newly designed game is introduced, which feels like Backgammon, but has a simplified rule set. Unlike earlier attempts at simplifying the game, Nannon maintains enough features a...
Jordan B. Pollack
SASO
2008
IEEE
15 years 4 months ago
Self-Adaptive Dissemination of Data in Dynamic Sensor Networks
The distribution of data in large dynamic wireless sensor networks presents a difficult problem due to node mobility, link failures, and traffic congestion. In this paper, we pr...
David Dorsey, Bjorn Jay Carandang, Moshe Kam, Chri...