Popular GitHub Repositories for Evolution Strategy

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning

Evolution API is an open-source WhatsApp integration API

MiniMax-01 is a simple implementation of the MiniMax algorithm, a widely used strategy for decision-making in two-player turn-based games like Tic-Tac-Toe. The algorithm aims to minimize the maximum possible loss for the player, making it a popular choice for developing AI opponents in various game scenarios.

An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks

Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm

Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

Evolutionary Algorithm using Python, 莫烦Python 中文AI教学

:rocket: Blazing fast neuro-evolution & backpropagation for the browser and Node.js

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

Jenetics - Genetic Algorithm, Genetic Programming, Grammatical Evolution, Evolutionary Algorithm, and Multi-objective Optimization

2024 up-to-date list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.

This code accompanies the the paper Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture (https://arxiv.org/pdf/2112.08534.pdf).

Long-Term Evolution Project of Reinforcement Learning

Evolving agents is a production-grade environment for orchestrating, evolving, and managing AI agents

SharpNEAT - Evolution of Neural Networks. A C# .NET Framework.

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).

lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.