GavinW3

Deep-Reinforcement-Learning-Hands-On

Hands-on Deep Reinforcement Learning, published by Packt

Deep-Reinforcement-Learning-Hands-On-Second-Edition

Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt

Deep-reinforcement-learning-with-pytorch

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

gti-mig-paper

HELLO-WORLD

THIS IS MY FIRST REPOSITORY HELLO WORLD IN GITHUB

leedeeprl-notes

李宏毅《深度强化学习》笔记，在线阅读地址：https://datawhalechina.github.io/leedeeprl-notes/

lstm-load-forecasting

Electricity load forecasting with LSTM (Recurrent Neural Network)

machine_learning_notebook

机器学习纯算法实现。持续更新

machineLearningDeepLearning

李宏毅2021机器学习深度学习笔记PPT作业

unit_commitment

An unit commitment optimization problem.

GavinW3/unit_commitment

An unit commitment optimization problem.

GavinW3/leedeeprl-notes

李宏毅《深度强化学习》笔记，在线阅读地址：https://datawhalechina.github.io/leedeeprl-notes/

GavinW3/Deep-Reinforcement-Learning-Hands-On

Hands-on Deep Reinforcement Learning, published by Packt

GavinW3/Deep-Reinforcement-Learning-Hands-On-Second-Edition

Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt

GavinW3/Deep-reinforcement-learning-with-pytorch

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

GavinW3/gti-mig-paper

GavinW3/HELLO-WORLD

THIS IS MY FIRST REPOSITORY HELLO WORLD IN GITHUB

GavinW3/lstm-load-forecasting

Electricity load forecasting with LSTM (Recurrent Neural Network)

GavinW3/machine_learning_notebook

机器学习纯算法实现。持续更新

GavinW3/machineLearningDeepLearning

李宏毅2021机器学习深度学习笔记PPT作业

GavinW3/Mathematical-Modeling-Algorithms-and-Applications

《数学建模算法与应用（第2版）》（Mathematical Modeling Algorithms and Applications） - 司守奎, 孙兆亮

GavinW3/rl4uc

Reinforcement learning for unit commitment

GavinW3/TCCgraduacao

Este repositório armazena os códigos MATLAB utilizados para desenvolver parte do trabalho final de graduação de Natália Teixeira

GavinW3/TensorFlow-Examples

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)