Popular GitHub Repositories for Mineral Exploration

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

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

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

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

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

Python implementations of contextual bandits algorithms

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.

A curated list of awesome exploration RL resources (continually updated)

Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

Code to reproduce the experiments in Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation (MEEE).

This is the pytorch implementation of Hindsight Experience Replay (HER) - Experiment on all fetch robotic environments.

My Exploration on Deep Reinforcement Learning Survey

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).

Pytorch code for NeurIPS-20 Paper "Object Goal Navigation using Goal-Oriented Semantic Exploration"