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AIPS
2008
13 years 10 months ago
Fast Planning for Dynamic Preferences
We present an algorithm that quickly finds optimal plans for unforeseen agent preferences within graph-based planning domains where actions have deterministic outcomes and action ...
Brian Ziebart, Anind K. Dey, J. Andrew Bagnell
NIPS
2004
13 years 9 months ago
Experts in a Markov Decision Process
We consider an MDP setting in which the reward function is allowed to change during each time step of play (possibly in an adversarial manner), yet the dynamics remain fixed. Simi...
Eyal Even-Dar, Sham M. Kakade, Yishay Mansour
ML
2008
ACM
152views Machine Learning» more  ML 2008»
13 years 7 months ago
Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
Abstract. We consider batch reinforcement learning problems in continuous space, expected total discounted-reward Markovian Decision Problems. As opposed to previous theoretical wo...
András Antos, Csaba Szepesvári, R&ea...
JGTOOLS
2007
144views more  JGTOOLS 2007»
13 years 7 months ago
Faster Relief Mapping using the Secant Method
Relief mapping using the secant method offers an efficient method for adding per pixel height field based displacement to an arbitrary polygonal mesh in real time. The technique...
Eric Risser, Musawir Shah, Sumanta N. Pattanaik
JMLR
2010
155views more  JMLR 2010»
13 years 2 months ago
Bayesian Gaussian Process Latent Variable Model
We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method...
Michalis Titsias, Neil D. Lawrence