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» Mean-Variance Optimization in Markov Decision Processes
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NIPS
2007
13 years 9 months ago
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs
We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). O...
Ambuj Tewari, Peter L. Bartlett
NIPS
2001
13 years 9 months ago
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...
Evan Greensmith, Peter L. Bartlett, Jonathan Baxte...
IJCAI
2003
13 years 9 months ago
Generalizing Plans to New Environments in Relational MDPs
A longstanding goal in planning research is the ability to generalize plans developed for some set of environments to a new but similar environment, with minimal or no replanning....
Carlos Guestrin, Daphne Koller, Chris Gearhart, Ne...
ICML
2007
IEEE
14 years 8 months ago
Conditional random fields for multi-agent reinforcement learning
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...
GLOBECOM
2007
IEEE
14 years 2 months ago
Cross-Layer Call Admission Control for a CDMA Uplink Employing a Base-Station Antenna Array
— A novel cross-layer call admission control policy is proposed for a general CDMA beamforming system. In contrast to previously proposed call admission control (CAC) policies wh...
Wei Sheng, Steven D. Blostein