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AGENTS
2001
Springer
13 years 12 months ago
Hierarchical multi-agent reinforcement learning
In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multi-agent tasks. We introduce a hierarchical multi-a...
Rajbala Makar, Sridhar Mahadevan, Mohammad Ghavamz...
VLDB
2001
ACM
66views Database» more  VLDB 2001»
13 years 12 months ago
An Evaluation of Generic Bulk Loading Techniques
Bulk loading refers to the process of creating an index from scratch for a given data set. This problem is well understood for B-trees, but so far, non-traditional index structure...
Jochen Van den Bercken, Bernhard Seeger
CA
1999
IEEE
13 years 11 months ago
Collaborative Animation over the Network
The continuously increasing complexity of computer animationsmakes it necessary to rely on the knowledge of various experts to cover the different areas of computer graphics and a...
François Faure, Chris Faisstnauer, Gerd Hes...
HPDC
1999
IEEE
13 years 11 months ago
Remote Application Scheduling on Metacomputing Systems
Efficient and robust metacomputing requires the decomposition of complex jobs into tasks that must be scheduled on distributed processing nodes. There are various ways of creating...
Heath A. James, Kenneth A. Hawick
IWANN
1999
Springer
13 years 11 months ago
Using Temporal Neighborhoods to Adapt Function Approximators in Reinforcement Learning
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
R. Matthew Kretchmar, Charles W. Anderson