Popular GitHub Repositories for Exploration Strategy

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

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

MS-Agent: Lightweight Framework for Empowering Agents with Autonomous Exploration

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

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

For deep RL and the future of AI.

[ICML 2017] TensorFlow code for Curiosity-driven Exploration for Deep Reinforcement Learning

DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration

Evolutionary Algorithm using Python, 莫烦Python 中文AI教学

Automatically find issues in image datasets and practice data-centric computer vision.

AIDE: AI-Driven Exploration in the Space of Code. State of the Art machine Learning engineering agents that automates AI R&D.

A 2D space exploration and mining game made with Godot and our AI framework

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

Pytorch code for ICLR-20 Paper "Learning to Explore using Active Neural SLAM"

Python implementations of contextual bandits algorithms

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

Feature exploration for supervised learning

Paddle Multimodal Integration and eXploration, supporting mainstream multi-modal tasks, including end-to-end large-scale multi-modal pretrain models and diffusion model toolbox. Equipped with high performance and flexibility.

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