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» Mean field for Markov Decision Processes: from Discrete to C...
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AIPS
2007
13 years 10 months ago
Learning to Plan Using Harmonic Analysis of Diffusion Models
This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
ICIP
2006
IEEE
14 years 1 months ago
A Theory of Aliasing Separation for Light Field Data
A light field means a 4-D function which characterizes the flow of light rays from a target scene, and used for image-based rendering. This paper presents a novel theoretical fr...
Keita Takahashi, Takeshi Naemura
ICML
2006
IEEE
14 years 8 months ago
PAC model-free reinforcement learning
For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm--Delayed Q-Learning. We prove it is PAC, achieving near o...
Alexander L. Strehl, Lihong Li, Eric Wiewiora, Joh...
AIPS
2006
13 years 9 months ago
Solving Factored MDPs with Exponential-Family Transition Models
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
Branislav Kveton, Milos Hauskrecht
AAAI
2006
13 years 9 months ago
Learning Basis Functions in Hybrid Domains
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
Branislav Kveton, Milos Hauskrecht