A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.

Time series anomaly detection algorithm implementations for TimeEval (Docker-based)

Code and documentation to train Stanford's Alpaca models, and generate the data.

A collection of offline reinforcement learning algorithms.

无线与深度学习结合的论文代码整理/Paper-with-Code-of-Wireless-communication-Based-on-DL

Awesome machine learning for combinatorial optimization papers.

Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

This repository contains most of pytorch implementation based classic deep reinforcement learning algorithms, including - DQN, DDQN, Dueling Network, DDPG, SAC, A2C, PPO, TRPO. (More algorithms are…

PyTorch C++ Reinforcement Learning

Enable macOS HiDPI and have a native setting.

A PyTorch Library for Meta-learning Research

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)

A Comprehensive Reinforcement Learning Zoo for Simple Usage 🚀

🚀 Awesome System for Machine Learning ⚡️ AI System Papers and Industry Practice. ⚡️ System for Machine Learning, LLM (Large Language Model), GenAI (Generative AI). 🍻 OSDI, NSDI, SIGCOMM, SoCC, MLSy…

An elegant PyTorch deep reinforcement learning library.

Collection of reinforcement learning algorithms

Pytorch implementations of RL algorithms, focusing on model-based, lifelong, reset-free, and offline algorithms. Official codebase for Reset-Free Lifelong Learning with Skill-Space Planning.

A high-performance distributed training framework for Reinforcement Learning

Python implementations of contextual bandits algorithms

TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

Implementation of Proximal Meta-Policy Search (ProMP) as well as related Meta-RL algorithm. Includes a useful experiment framework for Meta-RL.

InfoGAN implementation using tensorflow

Recurrent (conditional) generative adversarial networks for generating real-valued time series data.

Advanced Deep Learning and Reinforcement Learning course taught at UCL in partnership with Deepmind

The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) paper in Pytorch.

A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.

Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow