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NIPS
1996
15 years 4 months ago
Multidimensional Triangulation and Interpolation for Reinforcement Learning
Dynamic Programming, Q-learning and other discrete Markov Decision Process solvers can be applied to continuous d-dimensional state-spaces by quantizing the state space into an arr...
Scott Davies
87
Voted
HICSS
2009
IEEE
94views Biometrics» more  HICSS 2009»
15 years 10 months ago
Enhancing Learning Experiences in Partially Distributed Teams: Training Students to Work Effectively Across Distances
Three training modules were designed to decrease ingroup dynamics in Partially Distributed Teams, which have two or more geographically separated subteams. The action research ori...
Rosalie J. Ocker, Dana Kracaw, Starr Roxanne Hiltz...
76
Voted
ICRA
2007
IEEE
110views Robotics» more  ICRA 2007»
15 years 9 months ago
A Reinforcement Learning Approach to Lift Generation in Flapping MAVs: Experimental Results
— In [17] we proposed an RL framework for control of flapping-wing MAVs. The algorithm has been discussed and simulation results using a quasi-steady model showed initial promis...
Mehran Motamed, Joseph Yan
134
Voted
GLVLSI
2005
IEEE
103views VLSI» more  GLVLSI 2005»
15 years 9 months ago
Causal probabilistic input dependency learning for switching model in VLSI circuits
Switching model captures the data-driven uncertainty in logic circuits in a comprehensive probabilistic framework. Switching is a critical factor that influences dynamic, active ...
Nirmal Ramalingam, Sanjukta Bhanja
AICT
2006
IEEE
130views Communications» more  AICT 2006»
15 years 5 months ago
A Search Theoretical Approach to P2P Networks: Analysis of Learning
One of the main characteristics of the peer-to-peer systems is the highly dynamic nature of the users present in the system. In such a rapidly changing enviroment, end-user guaran...
Nazif Cihan Tas, Bedri Kamil Onur Tas