zhangmeihang625's starred repositories
gpt_academic
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。
tensor2tensor
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
Machine-Learning
:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归
LLM-Agent-Paper-List
The paper list of the 86-page paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al.
Informer2020
The GitHub repository for the paper "Informer" accepted by AAAI 2021.
Deep-reinforcement-learning-with-pytorch
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
modelscope-agent
ModelScope-Agent: An agent framework connecting models in ModelScope with the world
DRL-Pytorch
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
MultiObjectiveOptimization
Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"
time-series-forecasting-with-python
A use-case focused tutorial for time series forecasting with python
DRLPytorch
Pytorch for Deep Reinforcement Learning
Basopra
BASOPRA - BAttery Schedule OPtimizer for Residential Applications. Daily battery schedule optimizer (i.e. 24 h optimization framework), assuming perfect day-ahead forecast of the electricity demand load and solar PV generation in order to determine the maximum economic potential regardless of the forecast strategy used. Include the use of different applications which residential batteries can perform from a consumer perspective. Applications such as avoidance of PV curtailment, demand load-shifting and demand peak shaving are considered along with the base application, PV self-consumption. Different battery technologies and sizes can be analyzed as well as different tariff structures. Aging is treated as an exogenous parameter, calculated on daily basis and is not subject of optimization. Data with 15-minute temporal resolution are used for simulations. The model objective function have two components, the energy-based and the power-based component, as the tariff structure depends on the applications considered, a boolean parameter activate the power-based factor of the bill when is necessary.
multi_objective_optimization
Multi-objective optimization of operation planning of disitrict energy systems to minimize operating cost and emissions under uncertainties.
Python-Intelligent-Optimization-Algorithms
智能优化算法的python手动实现，注释详细
JSSP_actor-critic_Agasucci_Monaci_Grani
Code from the paper An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents."
Minimizing-the-Energy-Consumption-in-a-Server-with-Deep-Q-Learning
The Repostitory delves into the Optimization of Business Processes by Minimizing the Energy Consumption in a Server with Deep Q Learning
AIML425_Project
Final project for the course (Efficient GNN for DRL)