A framework that allows people to write their own Rocket League bots.

Overview

YOU PROBABLY SHOULDN'T PULL THIS REPO

Bot Makers Read This!

If you just want to make a bot, you don't need to be here. Instead, start with one of these:

If you just want to play with some bots, you can go to RLBotGUI to easily start matches with bots

Framework Contributors

This repository is currently incomplete to meet the legal needs of the Psyonix API. It is missing the source code behind RLBot.exe, and a few other files. If you want to make a code change that involves RLBot.exe (or the interface dll since it's closely related), you'll need some help from someone with access to the closed repo.

We expect we'll be able to open-source everything eventually, and then this repo will be back in business.

NOTICE: We intend to cherry-pick any commits you make here into the closed repo. At a later date, when we have permission to open-source everything, we will force push the closed repo to this master branch. Your commits will still be there with proper attribution, but if you have any work in progress, it will need to be rebased at that time.

RLBot

Framework Info

The RLBot framework helps people create bots for use in Rocket League's offline modes, just for fun. It provides values from the game like car and ball position, and carries back button presses. RLBot works for up to 10 bots reliably; it can be used up to 64, but can result in issues (bots disappearing after goals, spawning inside one another, etc).

Requirements

Rocket League, Python 3.6+

Quick Start

  1. Run setup.bat (or equivalent if you're on Linux or Mac)
  2. Open a terminal and execute python runner.py

Development Workflow

The first thing you'll want to do is run setup.bat. This does a lot of important things:

  • Sets up your rlbot installation in pip to link to your local files in this folder. Once you've done this, running rlbot from anywhere on your computer will reference these local files, including the dlls, etc.
  • Generates important code based on the .fbs message spec. Therefore it's a prerequisite for running anything.
  • Installs python package dependencies.

If you're doing work that affects our .dll or .exe files, you should also be aware of:

  • copy-dlls.bat - This copies the debug versions any built dlls from visual studio into the correct subdirectory in the python source folder.
  • copy-dlls-release.bat - This copies the release versions any built dlls from visual studio into the correct subdirectory in the python source folder.

For deploying changes, please see https://github.com/RLBot/RLBot/wiki/Deploying-Changes

When you're done with development and want to get back to the official rlbot version vended from https://pypi.org/project/rlbot/, the easiest way to do that is simply pip uninstall rlbot. Then the next time you execute a bat file from one of the RLBot*Example repos, a fresh copy will be installed from pip.

Wikis

There's tons of good information at https://github.com/RLBot/RLBot/wiki

Extras

Community Info

Video Example

Video

Tournament History

Tournament results are recorded in our braacket league.

Videos:

The best part

Psyonix Cone gave us a thumbs up! Thumbs up

EMNLP'2021: SimCSE: Simple Contrastive Learning of Sentence Embeddings

SimCSE: Simple Contrastive Learning of Sentence Embeddings This repository contains the code and pre-trained models for our paper SimCSE: Simple Contr

Princeton Natural Language Processing 2.5k Dec 29, 2022
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

VITA 112 Nov 07, 2022
GPU-accelerated Image Processing library using OpenCL

pyclesperanto pyclesperanto is a python package for clEsperanto - a multi-language framework for GPU-accelerated image processing. clEsperanto uses Op

17 Dec 25, 2022
Quantify the difference between two arbitrary curves in space

similaritymeasures Quantify the difference between two arbitrary curves Curves in this case are: discretized by inidviudal data points ordered from a

Charles Jekel 175 Jan 08, 2023
A collection of semantic image segmentation models implemented in TensorFlow

A collection of semantic image segmentation models implemented in TensorFlow. Contains data-loaders for the generic and medical benchmark datasets.

bobby 16 Dec 06, 2019
KDD CUP 2020 Automatic Graph Representation Learning: 1st Place Solution

KDD CUP 2020: AutoGraph Team: aister Members: Jianqiang Huang, Xingyuan Tang, Mingjian Chen, Jin Xu, Bohang Zheng, Yi Qi, Ke Hu, Jun Lei Team Introduc

96 May 30, 2022
Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt) Task Training huge unsupervised deep neural networks yields to strong progress in

Oliver Hahn 1 Jan 26, 2022
Sum-Product Probabilistic Language

Sum-Product Probabilistic Language SPPL is a probabilistic programming language that delivers exact solutions to a broad range of probabilistic infere

MIT Probabilistic Computing Project 57 Nov 17, 2022
Code for KHGT model, AAAI2021

KHGT Code for KHGT accepted by AAAI2021 Please unzip the data files in Datasets/ first. To run KHGT on Yelp data, use python labcode_yelp.py For Movi

32 Nov 29, 2022
Fake videos detection by tracing the source using video hashing retrieval.

Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos 🎉️ 📜 Directory Introduction VTL Trace Samples and Acc of Hash

56 Dec 22, 2022
This repository is related to an Arabic tutorial, within the tutorial we discuss the common data structure and algorithms and their worst and best case for each, then implement the code using Python.

Data Structure and Algorithms with Python This repository is related to the Arabic tutorial here, within the tutorial we discuss the common data struc

Mohamed Ayman 33 Dec 02, 2022
Build and run Docker containers leveraging NVIDIA GPUs

NVIDIA Container Toolkit Introduction The NVIDIA Container Toolkit allows users to build and run GPU accelerated Docker containers. The toolkit includ

NVIDIA Corporation 15.6k Jan 01, 2023
A Factor Model for Persistence in Investment Manager Performance

Factor-Model-Manager-Performance A Factor Model for Persistence in Investment Manager Performance I apply methods and processes similar to those used

Omid Arhami 1 Dec 01, 2021
The code for two papers: Feedback Transformer and Expire-Span.

transformer-sequential This repo contains the code for two papers: Feedback Transformer Expire-Span The training code is structured for long sequentia

Facebook Research 125 Dec 25, 2022
Unet network with mean teacher for altrasound image segmentation

Unet network with mean teacher for altrasound image segmentation

5 Nov 21, 2022
Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch

PyGAS: Auto-Scaling GNNs in PyG PyGAS is the practical realization of our G NN A uto S cale (GAS) framework, which scales arbitrary message-passing GN

Matthias Fey 139 Dec 25, 2022
Multi-label classification of retinal disorders

Multi-label classification of retinal disorders This is a deep learning course project. The goal is to develop a solution, using computer vision techn

Sundeep Bhimireddy 1 Jan 29, 2022
The codes and related files to reproduce the results for Image Similarity Challenge Track 1.

ISC-Track1-Submission The codes and related files to reproduce the results for Image Similarity Challenge Track 1. Required dependencies To begin with

Wenhao Wang 115 Jan 02, 2023
Experiments and examples converting Transformers to ONNX

Experiments and examples converting Transformers to ONNX This repository containes experiments and examples on converting different Transformers to ON

Philipp Schmid 4 Dec 24, 2022
Accepted at ICCV-2021: Workshop on Computer Vision for Automated Medical Diagnosis (CVAMD)

Is it Time to Replace CNNs with Transformers for Medical Images? Accepted at ICCV-2021: Workshop on Computer Vision for Automated Medical Diagnosis (C

Christos Matsoukas 80 Dec 27, 2022