TLoL-RL - League of Legends Reinforcement Learning Library

TLoL-RL (Reinforcement Learning Python Module) - League of Legends RL Module (Allows ML Models to Play League of Legends). It provides an interface for an agent to play League of Legends using an OpenAI Gym interface, similar to pylol which is originally based of pysc2 for Starcraft 2.

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 raokosan@gmail.com.

Quick Start Guide

Locally

To run an agent locally, you will first need to log into League of Legends. It is recommended to use a separate account from your main account when using the tlol-rl module as your main account may be detected as a bot.

Get TLoL-RL

From Source

You can install TLoL-RL from a local clone of the git repo:

git clone https://github.com/MiscellaneousStuff/tlol-rl.git
pip install -e tlol-rl/

Config

Once you have TLoL-RL installed, you will need to create a config.txt file. This needs to be located in the same working directory as any call to python -m tlol_rl.bin.agent or any other tlol_rl.bin script which relies on config.txt. The configuration file uses the following format:

[dirs]
tlol_rl_server = ..\Path\To\LView\
lol_client     = ..\Path\To\Riot Games\League of Legends\

Running

To test run the environment, go to where your config.txt file is and run:

python -m tlol_rl.bin.agent --champion "Ezreal"

You can replace "Ezreal" with any champion that your account owns! Between runs, you need to make sure that ConsoleApplication.exe has been stopped. Go to Task Manager and end the process if it is still running. You also need to make sure that dump.rdb is deleted if it exists in the same folder as config.txt. These issues will be fixed in the future.