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ICML
2001
IEEE
16 years 19 days 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
ICN
2007
Springer
15 years 6 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...
EWRL
2008
15 years 1 months ago
Efficient Reinforcement Learning in Parameterized Models: Discrete Parameter Case
We consider reinforcement learning in the parameterized setup, where the model is known to belong to a parameterized family of Markov Decision Processes (MDPs). We further impose ...
Kirill Dyagilev, Shie Mannor, Nahum Shimkin
UAI
2004
15 years 1 months ago
Region-Based Incremental Pruning for POMDPs
We present a major improvement to the incremental pruning algorithm for solving partially observable Markov decision processes. Our technique targets the cross-sum step of the dyn...
Zhengzhu Feng, Shlomo Zilberstein
ICMCS
2009
IEEE
149views Multimedia» more  ICMCS 2009»
14 years 9 months ago
A multi-agent framework for a hybrid dialog management system
The importance of dialog management systems has increased in recent years. Dialog systems are created for domain specific applications, so that a high demand for a flexible dialog...
Stefan Schwärzler, Joachim Schenk, Günth...