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» Constructing States for Reinforcement Learning
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ICML
2008
IEEE
14 years 9 months ago
An object-oriented representation for efficient reinforcement learning
Rich representations in reinforcement learning have been studied for the purpose of enabling generalization and making learning feasible in large state spaces. We introduce Object...
Carlos Diuk, Andre Cohen, Michael L. Littman
ATAL
2008
Springer
13 years 11 months ago
Switching dynamics of multi-agent learning
This paper presents the dynamics of multi-agent reinforcement learning in multiple state problems. We extend previous work that formally modelled the relation between reinforcemen...
Peter Vrancx, Karl Tuyls, Ronald L. Westra
AGI
2011
13 years 18 days ago
Reinforcement Learning and the Bayesian Control Rule
We present an actor-critic scheme for reinforcement learning in complex domains. The main contribution is to show that planning and I/O dynamics can be separated such that an intra...
Pedro Alejandro Ortega, Daniel Alexander Braun, Si...
ICRA
2010
IEEE
133views Robotics» more  ICRA 2010»
13 years 7 months ago
Generalized model learning for Reinforcement Learning on a humanoid robot
— Reinforcement learning (RL) algorithms have long been promising methods for enabling an autonomous robot to improve its behavior on sequential decision-making tasks. The obviou...
Todd Hester, Michael Quinlan, Peter Stone
SIGGRAPH
2010
ACM
14 years 1 months ago
Gesture controllers
We introduce gesture controllers, a method for animating the body language of avatars engaged in live spoken conversation. A gesture controller is an optimal-policy controller tha...
Sergey Levine, Philipp Krähenbühl, Sebastian Thr...