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ICONIP
2009
15 years 4 months ago
Tracking in Reinforcement Learning
Reinforcement learning induces non-stationarity at several levels. Adaptation to non-stationary environments is of course a desired feature of a fair RL algorithm. Yet, even if the...
Matthieu Geist, Olivier Pietquin, Gabriel Fricout
AAMAS
2005
Springer
15 years 12 months ago
Advice-Exchange Between Evolutionary Algorithms and Reinforcement Learning Agents: Experiments in the Pursuit Domain
This research aims at studying the effects of exchanging information during the learning process in Multiagent Systems. The concept of advice-exchange, introduced in (Nunes and Ol...
Luís Nunes, Eugénio C. Oliveira
FLAIRS
2007
15 years 8 months ago
A Generalizing Spatial Representation for Robot Navigation with Reinforcement Learning
In robot navigation tasks, the representation of the surrounding world plays an important role, especially in reinforcement learning approaches. This work presents a qualitative r...
Lutz Frommberger
IS
2010
15 years 4 months ago
Identifying user strategies in exploratory learning with evolving task modelling
Abstract—In this paper we present work on adaptive identification of learners’ strategies, gradually developing a higher level of adaptation based on evolving models of mathem...
Mihaela Cocea, George D. Magoulas
ICRA
2000
IEEE
111views Robotics» more  ICRA 2000»
15 years 10 months ago
Learning Globally Consistent Maps by Relaxation
Mobile robots require the ability to build their own maps to operate in unknown environments. A fundamental problem is that odometry-based dead reckoning cannot be used to assign ...
Tom Duckett, Stephen Marsland, Jonathan Shapiro