Scheduling BilinearRewards

Overview

Scheduling_BilinearRewards

Requirement

Python 3 >=3.5

Structure

  • main.py
    This file includes the main function.

    • For getting the results in Figure 1, please set variables for synthetic data in the main function as follows:
      I=10, J=2, T=700, d=2, mu_inv=1, rho_tot=1, n_tot=8, gamma=1.2, repeat=10, util_arriv=False, load=False, com=True, fix=False.
    • For getting the results in Figure 7, please set variables for real data in the main function as follows:
      I=5, J=12, d=4, T=1100, gamma=1.2, repeat=10, ext=False, prep=False, load=False, com=True.
  • Preprocess.py
    This file includes the code for extracting and preprocessing real data. It is required to put your own google cloud key in this file to extract the public dataset described in https://github.com/google/cluster-data. Otherwise, you can use the dataset in the 'data' file extracted from the public dataset by deactivating extraction in main.py (i.e. ext=False).

  • Environment.py
    This file includes the code for generating an environment (synthetic world or real world) of a queueing system with the bilinear reward structure.

  • Algorithm.py
    This file includes the code for scheduling algorithms.

  • Oracle.py
    This file includes the code for running the oracle policy.

How to run this code

Please run this command:

  • For synthetic data:
    python3 main.py syn

  • For real data:
    python3 main.py real

Owner
junghun.kim
junghun.kim
GNEE - GAT Neural Event Embeddings

GNEE - GAT Neural Event Embeddings This repository contains source code for the GNEE (GAT Neural Event Embeddings) method introduced in the paper: "Se

João Pedro Rodrigues Mattos 0 Sep 15, 2021
HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR. CVPR 2022

HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR. CVPR 2022 [Project page | Video] Getting sta

51 Nov 29, 2022
Aggragrating Nested Transformer Official Jax Implementation

NesT is a simple method, which aggragrates nested local transformers on image blocks. The idea makes vision transformers attain better accuracy, data efficiency, and convergence on the ImageNet bench

Google Research 169 Dec 20, 2022
ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation - SIGGRAPH 2021

ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation - SIGGRAPH 2021 Dataset Code Demos Authors: He Zhang, Yuting Ye, Tak

HE ZHANG 194 Dec 06, 2022
Code for Talking Face Generation by Adversarially Disentangled Audio-Visual Representation (AAAI 2019)

Talking Face Generation by Adversarially Disentangled Audio-Visual Representation (AAAI 2019) We propose Disentangled Audio-Visual System (DAVS) to ad

Hang_Zhou 750 Dec 23, 2022
Implementation of "Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification"

hypergraph_reid Implementation of "Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification" If you find this help your research,

62 Dec 21, 2022
A Deep Reinforcement Learning Framework for Stock Market Trading

DQN-Trading This is a framework based on deep reinforcement learning for stock market trading. This project is the implementation code for the two pap

61 Jan 01, 2023
Meta Language-Specific Layers in Multilingual Language Models

Meta Language-Specific Layers in Multilingual Language Models This repo contains the source codes for our paper On Negative Interference in Multilingu

Zirui Wang 20 Feb 13, 2022
Molecular Sets (MOSES): A benchmarking platform for molecular generation models

Molecular Sets (MOSES): A benchmarking platform for molecular generation models Deep generative models are rapidly becoming popular for the discovery

Neelesh C A 3 Oct 14, 2022
Training Structured Neural Networks Through Manifold Identification and Variance Reduction

Training Structured Neural Networks Through Manifold Identification and Variance Reduction This repository is a pytorch implementation of the Regulari

0 Dec 23, 2021
A concise but complete implementation of CLIP with various experimental improvements from recent papers

x-clip (wip) A concise but complete implementation of CLIP with various experimental improvements from recent papers Install $ pip install x-clip Usag

Phil Wang 515 Dec 26, 2022
A higher performance pytorch implementation of DeepLab V3 Plus(DeepLab v3+)

A Higher Performance Pytorch Implementation of DeepLab V3 Plus Introduction This repo is an (re-)implementation of Encoder-Decoder with Atrous Separab

linhua 326 Nov 22, 2022
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Official code of APHYNITY Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting (ICLR 2021, Oral) Yuan Yin*, Vincent Le Guen*

Yuan Yin 24 Oct 24, 2022
What can linearized neural networks actually say about generalization?

What can linearized neural networks actually say about generalization? This is the source code to reproduce the experiments of the NeurIPS 2021 paper

gortizji 11 Dec 09, 2022
D-NeRF: Neural Radiance Fields for Dynamic Scenes

D-NeRF: Neural Radiance Fields for Dynamic Scenes [Project] [Paper] D-NeRF is a method for synthesizing novel views, at an arbitrary point in time, of

Albert Pumarola 291 Jan 02, 2023
A code implementation of AC-GC: Activation Compression with Guaranteed Convergence, in NeurIPS 2021.

Code For AC-GC: Lossy Activation Compression with Guaranteed Convergence This code is intended to be used as a supplemental material for submission to

Dave Evans 2 Nov 01, 2022
High frequency AI based algorithmic trading module.

Flow Flow is a high frequency algorithmic trading module that uses machine learning to self regulate and self optimize for maximum return. The current

59 Dec 14, 2022
[ICML 2020] DrRepair: Learning to Repair Programs from Error Messages

DrRepair: Learning to Repair Programs from Error Messages This repo provides the source code & data of our paper: Graph-based, Self-Supervised Program

Michihiro Yasunaga 155 Jan 08, 2023
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Graph ConvNets in PyTorch October 15, 2017 Xavier Bresson http://www.ntu.edu.sg/home/xbresson https://github.com/xbresson https://twitter.com/xbresson

Xavier Bresson 287 Jan 04, 2023
Principled Detection of Out-of-Distribution Examples in Neural Networks

ODIN: Out-of-Distribution Detector for Neural Networks This is a PyTorch implementation for detecting out-of-distribution examples in neural networks.

189 Nov 29, 2022