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ATAL
2007
Springer
14 years 1 months ago
Model-based function approximation in reinforcement learning
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
Nicholas K. Jong, Peter Stone
CDC
2010
IEEE
138views Control Systems» more  CDC 2010»
13 years 2 months ago
Sensor-based robot deployment algorithms
Abstract-- In robot deployment problems, the fundamental issue is to optimize a steady state performance measure that depends on the spatial configuration of a group of robots. For...
Jerome Le Ny, George J. Pappas
GECCO
2005
Springer
142views Optimization» more  GECCO 2005»
14 years 29 days ago
Toward evolved flight
We present the first hardware-in-the-loop evolutionary optimization on an ornithopter. Our experiments demonstrate the feasibility of evolving flight through genetic algorithms an...
Rusty Hunt, Gregory Hornby, Jason D. Lohn
NIPS
1996
13 years 8 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
RTSS
1999
IEEE
13 years 11 months ago
A Scalable Solution to the Multi-Resource QoS Problem
The problem of maximizing system utility by allocating a single finite resource to satisfy discrete Quality of Service (QoS) requirements of multiple applications along multiple Q...
Chen Lee, John P. Lehoczky, Daniel P. Siewiorek, R...