Genetic Algorithms for Scalable RL

Genetic Algorithms for Scalable RL : r/reinforcementlearning - Reddit

I'm trying to build a scalable RL system that uses super-computers to train. But doesn't rely on the common assumption that environment evaluation is slow.

A scalable species-based genetic algorithm for reinforcement ...

A novel variant of genetic algorithm called SP-GA which utilizes a species-inspired weight initialization strategy and trains a population of deep neural ...

(PDF) A scalable species-based genetic algorithm for reinforcement ...

In this work, we leverage the benefits of EC and propose a novel variant of genetic algorithm called SP-GA which utilizes a species-inspired weight ...

A scalable species-based genetic algorithm for reinforcement learning

How effective are gradient-free evolutionary driven methods, like variants of Genetic Algorithms (GAs), in training deep neural networks for RL problems with a ...

RL-GA: A Reinforcement Learning-based Genetic Algorithm for ...

This paper proposes a mixed-integer programming model for the EDSSP problem and a genetic algorithm based on reinforcement learning (RL-GA).

Reinforcement learning without gradients: evolving agents using ...

Reinforcement learning without gradients: evolving agents using Genetic Algorithms ... During holidays I wanted to ramp up my reinforcement ...

[PDF] A scalable species-based genetic algorithm for reinforcement ...

A novel variant of genetic algorithm called SP-GA is proposed which utilizes a species-inspired weight initialization strategy and trains a population of ...

A scalable species-based genetic algorithm for reinforcement learning

In this work, we attempt to leverage the benefits of evolutionary computation as a competitive, scalable, and gradient-free alternative to ...

Evolution strategies as a scalable alternative to reinforcement learning

We've discovered that evolution strategies (ES), an optimization technique that's been known for decades, rivals the performance of standard reinforcement ...

Genetic Algorithm for Curriculum Generation in Multi-Agent...

Some related work are missing (see question 1 and 2). [1] Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning ...

Should I choose genetic algorithms or reinforcement learning? - Quora

Genetic algorithms (notably, CMA-ES) are typically employed to solve reinforcement learning problems through policy search.

Evolutionary algorithms a scalable alternative to reinforcement ...

What are evolutionary algorithms? Simplistically speaking they resemble genetic algorithms. They have a randomly generated pool of features ...

Deep Neuroevolution: Genetic Algorithms Are a Competitive ... - arXiv

That raises the question of whether non-gradient-based evolutionary algorithms can work at DNN scales. Here we demonstrate they can: we evolve ...

Scalable evolutionary hierarchical reinforcement learning

We introduce a novel method merging S-ES and HRL, which creates a highly scalable and efficient (compute time) algorithm. We demonstrate ...

yeshenpy/Awesome-Evolutionary-Reinforcement-Learning - GitHub

Arxiv: Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms This survey is now categorized to better align ...

Genetic-Algorithm-Aided Deep Reinforcement Learning for Multi ...

Comparative simulation results are provided to demonstrate the merit of the proposed method. The proposed Genetic-Algorithm-aided multi-agent DRL outperformed ...

Genetic Algorithm And Reinforcement Learning | Restackio

The integration of GA and RL represents a significant advancement in optimization techniques, providing a framework that is both flexible and ...

Evolutionary Reinforcement Learning: A Systematic Review ... - arXiv

The method consists of multi-agent reinforcement learning, Graph Neural Networks (GNNs), and genetic algorithms. The ERLGA [50] method discusses ...

Population based Reinforcement Learning - IEEE Xplore

Abstract: Genetic algorithms have recently seen an increase in application due to their highly scalable nature.

Using reinforcement learning for tuning genetic algorithms

Genetic algorithms (GAs) are a subclass of evolutionary algorithms often used to solve difficult combinatorial or non-linear problems.