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» Q-Decomposition for Reinforcement Learning Agents
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FLAIRS
1998
15 years 1 months ago
Learning to Race: Experiments with a Simulated Race Car
Our focus is on designing adaptable agents for highly dynamic environments. Wehave implementeda reinforcement learning architecture as the reactive componentof a twolayer control ...
Larry D. Pyeatt, Adele E. Howe
COST
2009
Springer
185views Multimedia» more  COST 2009»
14 years 9 months ago
How an Agent Can Detect and Use Synchrony Parameter of Its Own Interaction with a Human?
Synchrony is claimed by psychology as a crucial parameter of any social interaction: to give to human a feeling of natural interaction, a feeling of agency [17], an agent must be a...
Ken Prepin, Philippe Gaussier
ICML
2004
IEEE
16 years 17 days ago
Using relative novelty to identify useful temporal abstractions in reinforcement learning
lative Novelty to Identify Useful Temporal Abstractions in Reinforcement Learning ?Ozg?ur S?im?sek ozgur@cs.umass.edu Andrew G. Barto barto@cs.umass.edu Department of Computer Scie...
Özgür Simsek, Andrew G. Barto
ATAL
2009
Springer
15 years 6 months ago
Learning of coordination: exploiting sparse interactions in multiagent systems
Creating coordinated multiagent policies in environments with uncertainty is a challenging problem, which can be greatly simplified if the coordination needs are known to be limi...
Francisco S. Melo, Manuela M. Veloso
ICML
2007
IEEE
16 years 17 days ago
Automatic shaping and decomposition of reward functions
This paper investigates the problem of automatically learning how to restructure the reward function of a Markov decision process so as to speed up reinforcement learning. We begi...
Bhaskara Marthi