Solving Flexible Job Shop Scheduling by learning to dispatch with Deep Reinforcement Learning

Deep Reinforcement Learning Hands-On, 3E_Published by Packt

Transfer Learning in Reinforcement Learning using Stable-Baseline3 | Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations

Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation

Source Code for A Closer Look at Invalid Action Masking in Policy Gradient Algorithms

A holistic resource allocation across edge cloud; published in UCC '24

Python Implementation of Reinforcement Learning: An Introduction

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

A Gymnasium environment for simulating job scheduling in Apache Spark

Batsim is a scientific simulator commonly used to evaluate Resource and Job Management System (RJMS) policies. Batsim-py allows using Batsim from Python 3.

Implementation of the paper "A Reinforcement Learning Based Strategy for Dynamic Scheduling on Heterogeneous Platforms".

Learning in Noisy MDP (which is governed by stochastic, exogenous input processes) with input-dependent baseline

The python code for paper "Multi-objective Deep Reinforcement Learning for Mobile Edge Computing"

The source code for the paper titled Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing

FogBus2: A Lightweight and Distributed Container-based Framework for Integration of IoT-enabled Systems with Edge and Cloud Computing

Workload analysis on Alibaba Cluster Trace

Learning Scheduling Algorithms for Data Processing Clusters

Code for our paper on doing resource allocation with graph neural networks