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ICML
2004
IEEE
14 years 8 months ago
Learning to fly by combining reinforcement learning with behavioural cloning
Reinforcement learning deals with learning optimal or near optimal policies while interacting with the environment. Application domains with many continuous variables are difficul...
Eduardo F. Morales, Claude Sammut
ICONIP
2009
13 years 5 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
IAT
2008
IEEE
13 years 7 months ago
Scaling Up Multi-agent Reinforcement Learning in Complex Domains
TD-FALCON (Temporal Difference - Fusion Architecture for Learning, COgnition, and Navigation) is a class of self-organizing neural networks that incorporates Temporal Difference (...
Dan Xiao, Ah-Hwee Tan
CEC
2010
IEEE
13 years 7 months ago
Coevolutionary Temporal Difference Learning for small-board Go
—In this paper we apply Coevolutionary Temporal Difference Learning (CTDL), a hybrid of coevolutionary search and reinforcement learning proposed in our former study, to evolve s...
Krzysztof Krawiec, Marcin Szubert
ROBOCUP
2007
Springer
153views Robotics» more  ROBOCUP 2007»
14 years 1 months ago
Model-Based Reinforcement Learning in a Complex Domain
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu