Efficient reinforcement learning through symbiotic evolution

Eecient Reinforcement Learning through Symbiotic Evolution

This article presents a novel reinforcement learning method called SANE (Symbiotic, Adaptive Neuro-Evolution), which evolves a population of neurons through genetic algorithms to form a neural network capable of performing a task. Symbiotic evolution promotes both cooperation and specialization, which results in a fast, eecient genetic search and prevents convergence to subopti-mal solutions. I...

cient Reinforcement Learning through Symbiotic

This article presents a novel reinforcement learning method called SANE (Symbiotic, Adaptive Neuro-Evolution), which evolves a population of neurons through genetic algorithms to form a neural network capable of performing a task. Symbiotic evolution promotes both cooperation and specialization, which results in a fast, eecient genetic search and prevents convergence to subopti-mal solutions. I...

Genetic reinforcement learning through symbiotic evolution for fuzzy controller design

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control...

Eecient Reinforcement Learning through Symbiotic Evolution Editor: Leslie Pack Kaelbling

This article presents a new reinforcement learning method called SANE (Symbiotic, Adaptive Neuro-Evolution), which evolves a population of neurons through genetic algorithms to form a neural network capable of performing a task. Symbiotic evolution promotes both cooperation and specialization, which results in a fast, e cient genetic search and discourages convergence to suboptimal solutions. I...

Efficient Learning Through Evolution: Neural Programming and Internal Reinforcement