https://github.com/emmanuelmess/simplifiedrimworldrl

Very simple implementation of RimWorld's mechanics in a gym env to train a reinforcement learning algorithm
https://github.com/emmanuelmess/simplifiedrimworldrl

gym-environment reinforcement-learning

Last synced: about 1 month ago
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Very simple implementation of RimWorld's mechanics in a gym env to train a reinforcement learning algorithm

• Host: GitHub
• URL: https://github.com/emmanuelmess/simplifiedrimworldrl
• Owner: EmmanuelMess
• Created: 2020-11-15T18:13:20.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-11-19T23:08:52.000Z (almost 5 years ago)
• Last Synced: 2025-01-15T22:28:28.363Z (9 months ago)
• Topics: gym-environment, reinforcement-learning
• Language: Python
• Homepage:
• Size: 69.3 KB
• Stars: 2
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Simplified RimWorld Env for RL

Very simple implementation of RimWorld's mechanics in a gym env to train a reinforcement learning algorithm

* A single deep Q learning algo learns how to kill enemies

* The friends are allowed instant movement on a 10 by 10 grid

* New enemy wave is spawned each time the last one is fully killed

* At the start waves are 1 enemy, at the half mark it goes to 2

* At the start the AI controls 1 actor, at the half mark it goes to 2

* Shots are instants

* There is a single box that provides cover

Rewards:

* 1 per kill

* -1 per actor that is killed

* -0.0000002 for moving to an occupied spot

* -0.0000002 for shooting an empty spot

* -0.05 for not doing anything for 10 consecutive steps

* +0.5 for killing all enemies in a round

[![Chat on Telegram](https://img.shields.io/badge/Chat%20on-Telegram-brightgreen.svg)](https://t.me/EmmanuelsApps)  

## Graph



The half point decrease in scores is because the amount of initial enemies and actors are upped.

Thanks:

* [seungeunrho/minimalRL](https://github.com/seungeunrho/minimalRL)

* [Learning to be a Bot: Reinforcement Learning in Shooter Games (Michelle McPartland and Marcus Gallagher, 2008)](https://www.aaai.org/Papers/AIIDE/2008/AIIDE08-013.pdf)

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