Fast Reinforcement Learning through Eugenic Neuro-evolution
نویسندگان
چکیده
In this paper we introduce EuSANE, a novel reinforcement learning algorithm based on the SANE neuro-evolution method. It uses a global search algorithm, the Eugenic Algorithm, to optimize the selection of neurons to the hidden layer of SANE networks. The performance of EuSANE is evaluated in the two-pole balancing benchmark task, showing that EuSANE is signiicantly stronger than other reinforcement learning methods to date in this task.
منابع مشابه
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...
متن کاملEecient 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...
متن کاملcient 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...
متن کاملQ-Value Based Particle Swarm Optimization for Reinforcement Neuro- Fuzzy System Design
This paper proposes a combination of particle swarm optimization (PSO) and Q-value based safe reinforcement learning scheme for neuro-fuzzy systems (NFS). The proposed Q-value based particle swarm optimization (QPSO) fulfills PSO-based NFS with reinforcement learning; that is, it provides PSO-based NFS an alternative to learn optimal control policies under environments where only weak reinforce...
متن کامل