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105
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IROS
2007
IEEE
157views Robotics» more  IROS 2007»
15 years 7 months ago
Autonomous blimp control using model-free reinforcement learning in a continuous state and action space
— In this paper, we present an approach that applies the reinforcement learning principle to the problem of learning height control policies for aerial blimps. In contrast to pre...
Axel Rottmann, Christian Plagemann, Peter Hilgers,...
126
Voted
IROS
2009
IEEE
145views Robotics» more  IROS 2009»
15 years 7 months ago
Utilizing prior information to enhance self-supervised aerial image analysis for extracting parking lot structures
Abstract— Road network information (RNI) simplifies autonomous driving by providing strong priors about driving environments. Its usefulness has been demonstrated in the DARPA U...
Young-Woo Seo, Chris Urmson
EMNLP
2011
14 years 11 days ago
Bootstrapping Semantic Parsers from Conversations
Conversations provide rich opportunities for interactive, continuous learning. When something goes wrong, a system can ask for clarification, rewording, or otherwise redirect the...
Yoav Artzi, Luke S. Zettlemoyer
115
Voted
AAAI
1992
15 years 1 months ago
Automatic Programming of Robots Using Genetic Programming
The goal in automatic programming is to get a computer to perform a task by telling it what needs to be done, rather than by explicitly programming it. This paper considers the ta...
John R. Koza, James Rice
AGENTS
1999
Springer
15 years 5 months ago
Team-Partitioned, Opaque-Transition Reinforcement Learning
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...
Peter Stone, Manuela M. Veloso