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PG
2003
IEEE
15 years 12 months ago
A State Machine for Real-Time Cutting of Tetrahedral Meshes
We introduce an algorithm that consistently and accurately processes arbitrary intersections in tetrahedral meshes in real-time. The intersection surfaces are modeled up to the cu...
Daniel Bielser, Pascal Glardon, Matthias Teschner,...
ICML
2007
IEEE
16 years 7 months ago
Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation
Reinforcement learning algorithms can become unstable when combined with linear function approximation. Algorithms that minimize the mean-square Bellman error are guaranteed to co...
Chee Wee Phua, Robert Fitch
ICML
2008
IEEE
16 years 7 months ago
An object-oriented representation for efficient reinforcement learning
Rich representations in reinforcement learning have been studied for the purpose of enabling generalization and making learning feasible in large state spaces. We introduce Object...
Carlos Diuk, Andre Cohen, Michael L. Littman
ICML
2005
IEEE
16 years 7 months ago
Reinforcement learning with Gaussian processes
Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framew...
Yaakov Engel, Shie Mannor, Ron Meir
ICML
2001
IEEE
16 years 7 months ago
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce versio...
Nicholas Roy, Andrew McCallum