TLoL (Python Module) - League of Legends Deep Learning AI (Research and Development)

Related tags

Deep Learningtlol-py
Overview

TLoL-py - League of Legends Deep Learning Library

TLoL-py is the Python component of the TLoL League of Legends deep learning library. It provides a set of utility methods and classes to deal with League of Legends game playing, deep learning datasets and provides a library to build a deep learning agent which can play League of Legends.

This module is currently updated to patch 11.23.

About

Disclaimer: This project is not affiliated with Riot Games in any way.

If you are interested in using this project or are just curious, send an email to [email protected].

Quick Start Guide

Get TLoL-py

From Source

You can install the TLoL python module from a local clone of the git repo:

git clone https://github.com/MiscellaneousStuff/tlol-py.git
pip install --upgrade tlol-py/

Config

This module requires the EnableReplayApi=1 flag to be added to .\Config\game.cfg within the League of Legends installation directory, underneath the [General] section so it should like:

[General]
...
EnableReplayApi=1
You might also like...
A PyTorch Library for Accelerating 3D Deep Learning Research
A PyTorch Library for Accelerating 3D Deep Learning Research

Kaolin: A Pytorch Library for Accelerating 3D Deep Learning Research Overview NVIDIA Kaolin library provides a PyTorch API for working with a variety

A Real-Time-Strategy game for Deep Learning research
A Real-Time-Strategy game for Deep Learning research

Description DeepRTS is a high-performance Real-TIme strategy game for Reinforcement Learning research. It is written in C++ for performance, but provi

计算机视觉中用到的注意力模块和其他即插即用模块PyTorch Implementation Collection of Attention Module and Plug&Play Module

PyTorch实现多种计算机视觉中网络设计中用到的Attention机制,还收集了一些即插即用模块。由于能力有限精力有限,可能很多模块并没有包括进来,有任何的建议或者改进,可以提交issue或者进行PR。

Implementation of Invariant Point Attention, used for coordinate refinement in the structure module of Alphafold2, as a standalone Pytorch module
Implementation of Invariant Point Attention, used for coordinate refinement in the structure module of Alphafold2, as a standalone Pytorch module

Invariant Point Attention - Pytorch Implementation of Invariant Point Attention as a standalone module, which was used in the structure module of Alph

AbelNN: Deep Learning Python module from scratch

AbelNN: Deep Learning Python module from scratch I have implemented several neural networks from scratch using only Numpy. I have designed the module

A Pytree Module system for Deep Learning in JAX
A Pytree Module system for Deep Learning in JAX

Treex A Pytree-based Module system for Deep Learning in JAX Intuitive: Modules are simple Python objects that respect Object-Oriented semantics and sh

FTIR-Deep Learning - FTIR Deep Learning With Python

CANDIY-spectrum Human analyis of chemical spectra such as Mass Spectra (MS), Inf

A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks A Research-oriented Federated Learning Library and Benchmark Platform

Turi Create simplifies the development of custom machine learning models.
Turi Create simplifies the development of custom machine learning models.

Quick Links: Installation | Documentation | WWDC 2019 | WWDC 2018 Turi Create Check out our talks at WWDC 2019 and at WWDC 2018! Turi Create simplifie

Comments
  • dowloader.py is outdated

    dowloader.py is outdated

    Issue

    It seems that riot has patched this, or at least changed the way it allows us to download replays, or hopefully I am wrong.

    for example, these two game_ids

    • 4274782214
    • 4291035149

    These game id's are game id's of anywhere between plat2-challenger on the NA1 server.

    I am unable to download the replays for either of these, as viewing the metadata url via the LCU explorer shows that the state is incompatible

    url = f"https://127.0.0.1:{str(port)}/lol-replays/v1/metadata/{str(gameId)}"
    

    when sending a GETrequest to the metadata endpoint, you will also probably get this response:

    {
      "downloadProgress": 499155328,
      "gameId": 4291035149,
      "state": "incompatible"
    }
    

    Un-Related

    nit-pick: why not use HTTPBasicAuth instead of using base64? nit-pick: why not use .cmdline() on the league process? [ it gives you access to a list of every argument that was used when starting the .exe file, suchas the remote-auth-port and the auth-token [ used for authentication to the league client ]

    opened by nubonics 2
Releases(patch_12_3)
This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-grained Classification".

HA-in-Fine-Grained-Classification This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-g

16 Oct 29, 2022
Saeed Lotfi 28 Dec 12, 2022
A Broad Study on the Transferability of Visual Representations with Contrastive Learning

A Broad Study on the Transferability of Visual Representations with Contrastive Learning This repository contains code for the paper: A Broad Study on

Ashraful Islam 29 Nov 09, 2022
Code for the TASLP paper "PSLA: Improving Audio Tagging With Pretraining, Sampling, Labeling, and Aggregation".

PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation Introduction Getting Started FSD50K Recipe AudioSet Recipe Label E

Yuan Gong 84 Dec 27, 2022
(CVPR 2021) Lifting 2D StyleGAN for 3D-Aware Face Generation

Lifting 2D StyleGAN for 3D-Aware Face Generation Official implementation of paper "Lifting 2D StyleGAN for 3D-Aware Face Generation". Requirements You

Yichun Shi 66 Nov 29, 2022
Learning Confidence for Out-of-Distribution Detection in Neural Networks

Learning Confidence Estimates for Neural Networks This repository contains the code for the paper Learning Confidence for Out-of-Distribution Detectio

235 Jan 05, 2023
Model Agnostic Interpretability for Multiple Instance Learning

MIL Model Agnostic Interpretability This repo contains the code for "Model Agnostic Interpretability for Multiple Instance Learning". Overview Executa

Joe Early 10 Dec 17, 2022
This provides the R code and data to replicate results in "The USS Trustee’s risky strategy"

USSBriefs2021 This provides the R code and data to replicate results in "The USS Trustee’s risky strategy" by Neil M Davies, Jackie Grant and Chin Yan

1 Oct 30, 2021
Code of our paper "Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning"

CCOP Code of our paper Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning Requirement Install OpenSelfSup Install Detectron2

Chenhongyi Yang 21 Dec 13, 2022
95.47% on CIFAR10 with PyTorch

Train CIFAR10 with PyTorch I'm playing with PyTorch on the CIFAR10 dataset. Prerequisites Python 3.6+ PyTorch 1.0+ Training # Start training with: py

5k Dec 30, 2022
Implementation of Convolutional enhanced image Transformer

CeiT : Convolutional enhanced image Transformer This is an unofficial PyTorch implementation of Incorporating Convolution Designs into Visual Transfor

Rishikesh (ऋषिकेश) 82 Dec 13, 2022
PAthological QUpath Obsession - QuPath and Python conversations

PAQUO: PAthological QUpath Obsession Welcome to paquo 👋 , a library for interacting with QuPath from Python. paquo's goal is to provide a pythonic in

Bayer AG 60 Dec 31, 2022
Implementing a simplified copy of Shazam application from scratch using MinHashing and LSH.

Building Shazam from scratch In this repository we tried to implement a simplified copy of the Shazam application able to tell you the name of a song

Arturo Ghinassi 0 Nov 17, 2022
This repository contains code for the paper "Decoupling Representation and Classifier for Long-Tailed Recognition", published at ICLR 2020

Classifier-Balancing This repository contains code for the paper: Decoupling Representation and Classifier for Long-Tailed Recognition Bingyi Kang, Sa

Facebook Research 820 Dec 26, 2022
The fastest way to visualize GradCAM with your Keras models.

VizGradCAM VizGradCam is the fastest way to visualize GradCAM in Keras models. GradCAM helps with providing visual explainability of trained models an

58 Nov 19, 2022
Region-aware Contrastive Learning for Semantic Segmentation, ICCV 2021

Region-aware Contrastive Learning for Semantic Segmentation, ICCV 2021 Abstract Recent works have made great success in semantic segmentation by explo

Hanzhe Hu 30 Dec 29, 2022
《LightXML: Transformer with dynamic negative sampling for High-Performance Extreme Multi-label Text Classification》(AAAI 2021) GitHub:

LightXML: Transformer with dynamic negative sampling for High-Performance Extreme Multi-label Text Classification

76 Dec 05, 2022
ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers

ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers Official implementation of ViewFormer. ViewFormer is a NeRF-free neural rend

Jonáš Kulhánek 169 Dec 30, 2022
Official implementation of "A Unified Objective for Novel Class Discovery", ICCV2021 (Oral)

A Unified Objective for Novel Class Discovery This is the official repository for the paper: A Unified Objective for Novel Class Discovery Enrico Fini

Enrico Fini 118 Dec 26, 2022