0412zoe

0412zoe's Stars

• Quantum-Cheese/DeepReinforcementLearning_Pytorch

  Pytorch realization of multiple Deep Reinforcement Learning alogrithms(DQN,DDPG,TD3,PPO,A3C...) with openai gym

  Language:Python5311

• XinJingHao/PPO-Continuous-Pytorch

  A clean and robust Pytorch implementation of PPO on continuous action space.

  Language:Python11516

• Lizhi-sjtu/DRL-code-pytorch

  Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

  Language:Python1k171

• sush1996/DDPG_Fetch

  Exploring the performance of Prioritized Experience Replay (PER) with the DDPG+HER scheme on the Fetch Robotics Environemnt

  Language:Python152

• sweetice/Deep-reinforcement-learning-with-pytorch

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

  Language:Python3.9k841

• cog-imperial/OMLT

  Represent trained machine learning models as Pyomo optimization formulations

  Language:Python26357

• ShangtongZhang/DeepRL

  Modularized Implementation of Deep RL Algorithms in PyTorch

  Language:Python3.2k679

• Cernewein/heating-RL-agent

  A Pytorch DQN and DDPG implementation for a smart home energy management system under varying electricity price.

  Language:Jupyter Notebook5813

• matsilv/rl-offline-online-opt

  Language:Python4

• chauncygu/Safe-Reinforcement-Learning-Baselines

  The repository is for safe reinforcement learning baselines.

  Language:Jupyter Notebook46775

• p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

  PyTorch implementations of deep reinforcement learning algorithms and environments

  Language:Python5.6k1.2k

• iffiX/machin

  Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

  Language:Python39751

• sometimesstudy/two-stage-robust-optimization

  Two stage robust optimization using a column-and-constraint generation (C&CG) method

  Language:Python377

• BeardHealth/Combined-Heat-and-Power-System-Economic-Dispatch

  Deep reinforcement learning approaches for CHP system economic dispatch

  Language:Python7418

• sfujim/TD3

  Author's PyTorch implementation of TD3 for OpenAI gym tasks

  Language:Python1.7k433

• lryz0612/DRL-Energy-Management

  Deep reinforcement learning based energy management strategy for hybrid electric vehicle

  Language:Jupyter Notebook17548

• ShengrenHou/Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning

  The Source code for paper "Optimal Energy System Scheduling Combining Mixed-Integer Programming and Deep Reinforcement Learning". Safe reinforcement learning, energy management

  Language:Python8416

• PaddlePaddle/PARL

  A high-performance distributed training framework for Reinforcement Learning

  Language:Python3.2k820

• pinyanLiu/HomeEnergyManagementSystem_RL-env

  Language:Python181

• zcavic/RL_EnergyStorageScheduling

  Language:Python7

• ShengrenHou/DRL-for-Energy-Systems-Optimal-Scheduling

  The source code for Performance comparision of Deep RL algorithms for Energy Systems Optimal Scheduling

  Language:Python9423

• jramak/dual-adp-suc

  Code / data for the paper "A Dual Approximate Dynamic Programming Approach for Multi-stage Stochastic Unit Commitment" http://www.optimization-online.org/DB_HTML/2018/06/6672.html

  Language:MATLAB4113

• binary-husky/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。

  Language:Python64.1k7.9k

• huwenqing0606/RL-manufacturing

  Source code for the paper <Joint Control of Manufacturing and Onsite Microgrid System via Novel Neural-Network Integrated Reinforcement Learning Algorithms>

  Language:Jupyter Notebook214

• tahanakabi/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.

  Language:HTML17843