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» Reinforcement Learning: An Introduction
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CORR
1998
Springer
164views Education» more  CORR 1998»
14 years 11 months ago
Training Reinforcement Neurocontrollers Using the Polytope Algorithm
A new training algorithm is presented for delayed reinforcement learning problems that does not assume the existence of a critic model and employs the polytope optimization algorit...
Aristidis Likas, Isaac E. Lagaris
ESANN
2006
15 years 1 months ago
Reducing policy degradation in neuro-dynamic programming
We focus on neuro-dynamic programming methods to learn state-action value functions and outline some of the inherent problems to be faced, when performing reinforcement learning in...
Thomas Gabel, Martin Riedmiller
AIIDE
2008
15 years 2 months ago
Learning to be a Bot: Reinforcement Learning in Shooter Games
This paper demonstrates the applicability of reinforcement learning for first person shooter bot artificial intelligence. Reinforcement learning is a machine learning technique wh...
Michelle McPartland, Marcus Gallagher
80
Voted
NECO
2002
105views more  NECO 2002»
14 years 11 months ago
Multiple Model-Based Reinforcement Learning
We propose a modular reinforcement learning architecture for non-linear, nonstationary control tasks, which we call multiple model-based reinforcement learning (MMRL). The basic i...
Kenji Doya, Kazuyuki Samejima, Ken-ichi Katagiri, ...
IAT
2003
IEEE
15 years 5 months ago
Asymmetric Multiagent Reinforcement Learning
A gradient-based method for both symmetric and asymmetric multiagent reinforcement learning is introduced in this paper. Symmetric multiagent reinforcement learning addresses the ...
Ville Könönen