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GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
15 years 9 months ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein
COLT
2000
Springer
15 years 8 months ago
Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning
We model reinforcement learning as the problem of learning to control a Partially Observable Markov Decision Process (  ¢¡¤£¦¥§  ), and focus on gradient ascent approache...
Peter L. Bartlett, Jonathan Baxter
PKDD
2009
Springer
129views Data Mining» more  PKDD 2009»
15 years 11 months ago
Considering Unseen States as Impossible in Factored Reinforcement Learning
Abstract. The Factored Markov Decision Process (FMDP) framework is a standard representation for sequential decision problems under uncertainty where the state is represented as a ...
Olga Kozlova, Olivier Sigaud, Pierre-Henri Wuillem...
IAT
2007
IEEE
15 years 11 months ago
Noise Tolerance in Reinforcement Learning Algorithms
This paper proposes a mechanism of noise tolerance for reinforcement learning algorithms. An adaptive agent that employs reinforcement learning algorithms may receive and accumula...
Richardson Ribeiro, Alessandro L. Koerich, Fabr&ia...
ROBOCUP
2007
Springer
102views Robotics» more  ROBOCUP 2007»
15 years 10 months ago
Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents
This paper describes the design and implementation of robotic agents for the RoboCup Simulation 2D category that learns using a recently proposed Heuristic Reinforcement Learning a...
Luiz A. Celiberto, Carlos H. C. Ribeiro, Anna Hele...