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» Using inaccurate models in reinforcement learning
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ICML
2006
IEEE
14 years 8 months ago
An intrinsic reward mechanism for efficient exploration
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal policy for later use? In other words, how should it explore, to be able to exp...
Özgür Simsek, Andrew G. Barto
ATAL
2004
Springer
14 years 1 months ago
A Pheromone-Based Utility Model for Collaborative Foraging
Multi-agent research often borrows from biology, where remarkable examples of collective intelligence may be found. One interesting example is ant colonies’ use of pheromones as...
Liviu Panait, Sean Luke
JMLR
2010
148views more  JMLR 2010»
13 years 2 months ago
A Generalized Path Integral Control Approach to Reinforcement Learning
With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical tec...
Evangelos Theodorou, Jonas Buchli, Stefan Schaal
FSR
2003
Springer
123views Robotics» more  FSR 2003»
14 years 28 days ago
Learning Predictions of the Load-Bearing Surface for Autonomous Rough-Terrain Navigation in Vegetation
Current methods for off-road navigation using vehicle and terrain models to predict future vehicle response are limited by the accuracy of the models they use and can suffer if th...
Carl Wellington, Anthony Stentz
ICMAS
1998
13 years 9 months ago
The Moving Target Function Problem in Multi-Agent Learning
We describe a framework that can be used to model and predict the behavior of MASs with learning agents. It uses a difference equation for calculating the progression of an agent&...
José M. Vidal, Edmund H. Durfee