jianzuo

DRL-Energy-Management

Deep reinforcement learning based energy management strategy for hybrid electric vehicle

ElegantRL

Lightweight and scalable deep reinforcement learning using PyTorch. 🔥

energy-py

Reinforcement learning for energy systems

ai_lib

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

Autoformer

About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008

CityLearn

Official reinforcement learning environment for demand response and load shaping

Conditional-Sig-Wasserstein-GANs

cv

My academic CV powered by LaTeX

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被全球200所大学采用教学。

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

DRL-for-microgrid-energy-management

We study the performance of various deep reinforcement learning algorithms for the problem of microgrid’s energy management system. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a population of thermostatically controlled loads, a population of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate between the different sources of flexibility by defining the priority resources, the direct demand control signals and the electricity prices. Seven deep reinforcement learning algorithms are implemented and empirically compared in this paper. The numerical results show a significant difference between the different deep reinforcement learning algorithms in their ability to converge to optimal policies. By adding an experience replay and a second semi-deterministic training phase to the well-known Asynchronous advantage actor critic algorithm, we achieved considerably better performance and converged to superior policies in terms of energy efficiency and economic value.

fastbook

The fastai book, published as Jupyter Notebooks

gym-anm

Design Reinforcement Learning environments that model Active Network Management (ANM) tasks in electricity distribution networks.

gym-anm-exp

Run MPC-based policies and train RL agents in gym-anm environments using implementations from Stable Baselines 3.

hands-on-rl

Free course that takes you from zero to Reinforcement Learning PRO 🦸🏻‍🦸🏽

homework_fall2021

Assignments for Berkeley CS 285: Deep Reinforcement Learning (Fall 2021)

IEEE-2014-datasets

Informer2020

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

jMetal

jMetal: a framework for multi-objective optimization with metaheuristics

lecture-julia.myst

Lecture source for "Quantitative Economics with Julia"

LTSF-Linear

This is the official implementation for AAAI-23 paper "Are Transformers Effective for Time Series Forecasting?"

malib

A parallel framework for population-based multi-agent reinforcement learning.

msppy

pytorch-Deep-Learning

Deep Learning (with PyTorch)

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

ReliabilityBookScripts

In this repository you will find scripts, datasets, examples developped in the book "System Reliability Theory; Models, Statistical Methods and Applications" by M. Rausand, A. Barros, A. Høyland (Wiley Edition).

resume

个人中文简历 Latex 源码 https://hijiangtao.github.io/

SCINet

The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“.

timegan-pytorch

This repository is a non-official implementation of TimeGAN (Yoon et al., NIPS2019) using PyTorch.