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NIPS
2003
13 years 9 months ago
Policy Search by Dynamic Programming
We consider the policy search approach to reinforcement learning. We show that if a “baseline distribution” is given (indicating roughly how often we expect a good policy to v...
J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng, Jeff...
ICML
2001
IEEE
14 years 8 months ago
Convergence of Gradient Dynamics with a Variable Learning Rate
As multiagent environments become more prevalent we need to understand how this changes the agent-based paradigm. One aspect that is heavily affected by the presence of multiple a...
Michael H. Bowling, Manuela M. Veloso
AAAI
2008
13 years 10 months ago
Adaptive Management of Air Traffic Flow: A Multiagent Coordination Approach
This paper summarizes recent advances in the application of multiagent coordination algorithms to air traffic flow management. Indeed, air traffic flow management is one of the fu...
Kagan Tumer, Adrian K. Agogino
IBERAMIA
2010
Springer
13 years 6 months ago
Dynamic Reward Shaping: Training a Robot by Voice
Reinforcement Learning is commonly used for learning tasks in robotics, however, traditional algorithms can take very long training times. Reward shaping has been recently used to ...
Ana C. Tenorio-Gonzalez, Eduardo F. Morales, Luis ...
ATAL
2005
Springer
14 years 1 months ago
Multi-agent reward analysis for learning in noisy domains
In many multi agent learning problems, it is difficult to determine, a priori, the agent reward structure that will lead to good performance. This problem is particularly pronoun...
Adrian K. Agogino, Kagan Tumer