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2007
136views Robotics» more  RSS 2007»
13 years 8 months ago
The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty
— We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a ...
Ron Alterovitz, Thierry Siméon, Kenneth Y. ...
CORR
2010
Springer
105views Education» more  CORR 2010»
13 years 5 months ago
Optimism in Reinforcement Learning Based on Kullback-Leibler Divergence
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...
Sarah Filippi, Olivier Cappé, Aurelien Gari...
ATAL
2009
Springer
14 years 1 months ago
Planning with continuous resources for agent teams
Many problems of multiagent planning under uncertainty require distributed reasoning with continuous resources and resource limits. Decentralized Markov Decision Problems (Dec-MDP...
Janusz Marecki, Milind Tambe
GLOBECOM
2008
IEEE
14 years 1 months ago
Foresighted Resource Reciprocation Strategies in P2P Networks
—We consider peer-to-peer (P2P) networks, where multiple peers are interested in sharing content. While sharing resources, autonomous and self-interested peers need to make decis...
Hyunggon Park, Mihaela van der Schaar
FLAIRS
2004
13 years 8 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber