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JMLR
2002
125views more  JMLR 2002»
14 years 9 months ago
Lyapunov Design for Safe Reinforcement Learning
Lyapunov design methods are used widely in control engineering to design controllers that achieve qualitative objectives, such as stabilizing a system or maintaining a system'...
Theodore J. Perkins, Andrew G. Barto
CEC
2008
IEEE
14 years 11 months ago
Learning benefits evolution if sex gives pleasure
Abstract-- In this paper we investigate the effects of individual learning on an evolving population of situated agents. We work with a novel type of system where agents can decide...
Robert Griffioen, Selmar K. Smit, A. E. Eiben
ECML
2004
Springer
15 years 2 months ago
Batch Reinforcement Learning with State Importance
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
Lihong Li, Vadim Bulitko, Russell Greiner
AAAI
2006
14 years 11 months ago
On the Difficulty of Modular Reinforcement Learning for Real-World Partial Programming
In recent years there has been a great deal of interest in "modular reinforcement learning" (MRL). Typically, problems are decomposed into concurrent subgoals, allowing ...
Sooraj Bhat, Charles Lee Isbell Jr., Michael Matea...
EUROCAST
2007
Springer
182views Hardware» more  EUROCAST 2007»
15 years 3 months ago
A k-NN Based Perception Scheme for Reinforcement Learning
Abstract a paradigm of modern Machine Learning (ML) which uses rewards and punishments to guide the learning process. One of the central ideas of RL is learning by “direct-online...
José Antonio Martin H., Javier de Lope Asia...