A Competitive Time-Trial AI for Need for Speed: Most Wanted Using Deep Reinforcement Learning (38c3)

All challenges and achievements in creating a competitive time-trial AI in NFS:MW. 15 years ago, at the height of my eSports career, I uploaded an (unofficial) ESL record at Need for Speed: Most Wanted (2005) (NFS:MW) to Youtube. In the meantime Deep Reinforcement Learning became popular and ever since I have dreamt of creating a competitive AI for my favorite racing game of all time: NFS:MW. Now finally the time was right: The hardware is fast enough, good software is available, and Sony's AI research has proven the task is actually doable. Hence I thought: "How hard can it possibly be?". This talk will present in detail all challenges and achievements in creating a competitive time-trial AI in NFS:MW from scratch - including but not limited to - hacking of the game to create a custom API, building a custom (real-time) OpenAI gym environment, steering the game using a virtual controller, and finally successfully training an AI using the Soft-Actor-Critic algorithm. All code including the API is written in Python and is open source. Licensed to the public under http://creativecommons.org/licenses/by/4.0 about this event: https://events.ccc.de/congress/2024/hub/event/a-competitive-time-trial-ai-for-need-for-speed-most-wanted-using-deep-reinforcement-learning/