CityLearn Challenge Multi-Agent Reinforcement Learning for Intelligent Energy Management, 2020, PikaPika team

Last update: Oct 10, 2022

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Deep Learning citylearn-2020-pikapika

Overview

Citylearn Challenge

This is the PyTorch implementation for PikaPika team, CityLearn Challenge Multi-Agent Reinforcement Learning for Intelligent Energy Management, 2020

Credits

Design: Jie Fu, Bingchan Zhao, Yunbo Wang

Implementation: Bingchan Zhao, Yunbo Wang

Discussion: Jie Fu, Bingchan Zhao, Yunbo Wang, Hao Dong, Zihan Ding

Lead: Jie Fu, Hao Dong

Owner

bigAIdream projects

Source code for my research projects

GitHub Repository

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CityLearn Challenge Multi-Agent Reinforcement Learning for Intelligent Energy Management, 2020, PikaPika team

Related tags

Overview

Citylearn Challenge

Credits

Owner

bigAIdream projects

Official implementation of Self-supervised Image-to-text and Text-to-image Synthesis

Compare neural networks by their feature similarity

This tool uses Deep Learning to help you draw and write with your hand and webcam.

Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

(CVPR 2022) Pytorch implementation of "Self-supervised transformers for unsupervised object discovery using normalized cut"

Focal and Global Knowledge Distillation for Detectors

This is the repository for our paper SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking

Learning to Reconstruct 3D Manhattan Wireframes from a Single Image

这是一个yolox-pytorch的源码，可以用于训练自己的模型。

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

Code for "Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans" CVPR 2021 best paper candidate

Nvidia Semantic Segmentation monorepo

A Gura parser implementation for Python

CONditionals for Ordinal Regression and classification in tensorflow

Image Super-Resolution by Neural Texture Transfer

Semi-automated OpenVINO benchmark_app with variable parameters

Auto HMM: Automatic Discrete and Continous HMM including Model selection

Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"

Reference implementation for Structured Prediction with Deep Value Networks

Optimus: the first large-scale pre-trained VAE language model