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ICML
2001
IEEE
14 years 9 months ago
Continuous-Time Hierarchical Reinforcement Learning
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
Mohammad Ghavamzadeh, Sridhar Mahadevan
ALDT
2009
Springer
140views Algorithms» more  ALDT 2009»
14 years 3 months ago
Directional Decomposition of Multiattribute Utility Functions
Abstract. Several schemes have been proposed for compactly representing multiattribute utility functions, yet none seems to achieve the level of success achieved by Bayesian and Ma...
Ronen I. Brafman, Yagil Engel
ICN
2007
Springer
14 years 2 months ago
Heuristic Approach of Optimal Code Allocation in High Speed Downlink Packet Access Networks
— In this paper, we use the Markov Decision Process (MDP) technique to find the optimal code allocation policy in High-Speed Downlink Packet Access (HSDPA) networks. A discrete ...
Hussein Al-Zubaidy, Jerome Talim, Ioannis Lambadar...
AAAI
2008
13 years 11 months ago
Interaction Structure and Dimensionality Reduction in Decentralized MDPs
Decentralized Markov Decision Processes are a powerful general model of decentralized, cooperative multi-agent problem solving. The high complexity of the general problem leads to...
Martin Allen, Marek Petrik, Shlomo Zilberstein
WSC
2008
13 years 11 months ago
On step sizes, stochastic shortest paths, and survival probabilities in Reinforcement Learning
Reinforcement Learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the probl...
Abhijit Gosavi