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ICML
1998
IEEE
14 years 8 months ago
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games t...
Junling Hu, Michael P. Wellman
ICML
2006
IEEE
14 years 8 months ago
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
Recent decision-theoric planning algorithms are able to find optimal solutions in large problems, using Factored Markov Decision Processes (fmdps). However, these algorithms need ...
Thomas Degris, Olivier Sigaud, Pierre-Henri Wuille...
ECAI
2008
Springer
13 years 9 months ago
A Simulation-based Approach for Solving Generalized Semi-Markov Decision Processes
Time is a crucial variable in planning and often requires special attention since it introduces a specific structure along with additional complexity, especially in the case of dec...
Emmanuel Rachelson, Gauthier Quesnel, Fréd&...
NIPS
2001
13 years 9 months ago
The Steering Approach for Multi-Criteria Reinforcement Learning
We consider the problem of learning to attain multiple goals in a dynamic environment, which is initially unknown. In addition, the environment may contain arbitrarily varying ele...
Shie Mannor, Nahum Shimkin
AIIDE
2009
13 years 8 months ago
Examining Extended Dynamic Scripting in a Tactical Game Framework
Dynamic scripting is a reinforcement learning algorithm designed specifically to learn appropriate tactics for an agent in a modern computer game, such as Neverwinter Nights. This...
Jeremy Ludwig, Arthur Farley