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acme

A library of reinforcement learning components and agents

ag_ieee123bus

alphazero_gomoku

An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

ant-learn-python-concurrent

Python并发编程专题

cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

cascadingfailuresimulation

Simulation Testbed for Cascading Failures in Power Systems

case_ieee123

A modified IEEE 123 bus feeder in matpower case format(mpc) modified from https://github.com/LucienBobo/SOCP_OPF_Nick2017

cloudsimpy

CloudSimPy: Datacenter job scheduling simulation framework

cluster-data

Borg cluster traces from Google

clusterdata

cluster data collected from production clusters in Alibaba for cluster management research

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

cs5890_microgrid

Reinforcement learning program for microgrid optimization

data_driven_control

ddpg_td3_results

ddz-ai

以孤立语假设和宽度优先搜索为基础，构建了一种多通道堆叠注意力Transformer结构的斗地主ai

deep-reinforcement-learning

《深度强化学习：原理与实践》，Code of the book <Deep Reinforcement Learning: Principles and Practices>

deep-reinforcement-learning-algorithms-with-pytorch

PyTorch implementations of deep reinforcement learning algorithms and environments

deep-reinforcement-learning-with-pytorch

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

deep-rl

Collection of Deep Reinforcement Learning algorithms

deep_rl

PyTorch implementations of Deep Reinforcement Learning algorithms (DQN, DDQN, A2C, VPG, TRPO, PPO, DDPG, TD3, SAC, ASAC, TAC, ATAC)

deeplearning

deeplearning_ai_books

deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

deeprl_network

multi-agent deep reinforcement learning for networked system control.

deer

DEEp Reinforcement learning framework

dl_rl_zoo

Lightweight, stable, efficient PyTorch implement of reinforcement learning. I want to call this PyTorch implement as "3-Python-file-RL".

drl

Deep Reinforcement Learning

drl-energy-management

Deep reinforcement learning based energy management strategy for hybrid electric vehicle

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.

drlib

DRLib：A concise deep reinforcement learning library, integrating HER and PER for almost off policy RL algos.

easy-rl

强化学习中文教程（蘑菇书），在线阅读地址：https://datawhalechina.github.io/easy-rl/