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ATAL
2007
Springer
14 years 1 months ago
IFSA: incremental feature-set augmentation for reinforcement learning tasks
Reinforcement learning is a popular and successful framework for many agent-related problems because only limited environmental feedback is necessary for learning. While many algo...
Mazda Ahmadi, Matthew E. Taylor, Peter Stone
GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
12 years 11 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
14 years 1 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen
AGI
2008
13 years 9 months ago
An Integrative Methodology for Teaching Embodied Non-Linguistic Agents, Applied to Virtual Animals in Second Life
A teaching methodology called Imitative-Reinforcement-Corrective (IRC) learning is described, and proposed as a general approach for teaching embodied non-linguistic AGI systems. I...
Ben Goertzel, Cassio Pennachin, Nil Geisweiller, M...
ICML
2000
IEEE
14 years 8 months ago
Eligibility Traces for Off-Policy Policy Evaluation
Eligibility traces have been shown to speed reinforcement learning, to make it more robust to hidden states, and to provide a link between Monte Carlo and temporal-difference meth...
Doina Precup, Richard S. Sutton, Satinder P. Singh