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AAAI
2010
13 years 11 months ago
Integrating Sample-Based Planning and Model-Based Reinforcement Learning
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...
IJCAI
2001
13 years 11 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Ronen I. Brafman, Moshe Tennenholtz
PAMI
1998
87views more  PAMI 1998»
13 years 9 months ago
Learning Local Languages and Their Application to DNA Sequence Analysis
—This paper concerns an efficient algorithm for learning in the limit a special type of regular languages called strictly locally testable languages from positive data, and its a...
Takashi Yokomori, Satoshi Kobayashi
TSMC
2010
13 years 4 months ago
Active Learning of Plans for Safety and Reachability Goals With Partial Observability
Traditional planning assumes reachability goals and/or full observability. In this paper, we propose a novel solution for safety and reachability planning with partial observabilit...
Wonhong Nam, Rajeev Alur
PAMI
2012
12 years 1 days ago
Task-Driven Dictionary Learning
—Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal proce...
Julien Mairal, Francis Bach, Jean Ponce