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CORR
2000
Springer
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9 years 11 months ago
Predicting the expected behavior of agents that learn about agents: the CLRI framework
We describe a framework and equations used to model and predict the behavior of multi-agent systems (MASs) with learning agents. A difference equation is used for calculating the ...
José M. Vidal, Edmund H. Durfee
ICMAS
1998
10 years 1 months ago
The Moving Target Function Problem in Multi-Agent Learning
We describe a framework that can be used to model and predict the behavior of MASs with learning agents. It uses a difference equation for calculating the progression of an agent&...
José M. Vidal, Edmund H. Durfee
IJCAI
2007
10 years 1 months ago
Building Portable Options: Skill Transfer in Reinforcement Learning
The options framework provides a method for reinforcement learning agents to build new high-level skills. However, since options are usually learned in the same state space as the...
George Konidaris, Andrew G. Barto
AAAI
2010
10 years 1 months ago
Reinforcement Learning via AIXI Approximation
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
Joel Veness, Kee Siong Ng, Marcus Hutter, David Si...
ACMACE
2006
ACM
10 years 5 months ago
Motivated reinforcement learning for non-player characters in persistent computer game worlds
Massively multiplayer online computer games are played in complex, persistent virtual worlds. Over time, the landscape of these worlds evolves and changes as players create and pe...
Kathryn Elizabeth Merrick, Mary Lou Maher
IJCNN
2006
IEEE
10 years 5 months ago
Learning a Rendezvous Task with Dynamic Joint Action Perception
Abstract— Groups of reinforcement learning agents interacting in a common environment often fail to learn optimal behaviors. Poor performance is particularly common in environmen...
Nancy Fulda, Dan Ventura
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