Dynamic Multi-Team Racing: Competitive Driving on 1/10-th Scale Vehicles via Learning in SimulationDownload PDF

Published: 30 Aug 2023, Last Modified: 16 Oct 2023CoRL 2023 PosterReaders: Everyone
Keywords: Multi-Agent, Reinforcement Learning, Sim-to-Real Transfer, Autonomous Racing
TL;DR: We learn dynamic 2-vs-2 multi-team racing via hierarchical policies through self-play reinforcement learning, and demonstrate that the competitive emergent behavior transfers to hardware.
Abstract: Autonomous racing is a challenging task that requires vehicle handling at the dynamic limits of friction. While single-agent scenarios like Time Trials are solved competitively with classical model-based or model-free feedback control, multi-agent wheel-to-wheel racing poses several challenges including planning over unknown opponent intentions as well as negotiating interactions under dynamic constraints. We propose to address these challenges via a learning-based approach that effectively combines model-based techniques, massively parallel simulation, and self-play reinforcement learning to enable zero-shot sim-to-real transfer of highly dynamic policies. We deploy our algorithm in wheel-to-wheel multi-agent races on scale hardware to demonstrate the efficacy of our approach. Further details and videos can be found on the project website: https://sites.google.com/view/dynmutr/home.
Student First Author: yes
Supplementary Material: zip
Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)
Video: https://www.youtube.com/watch?v=HbDL8EWZ5h8
Website: https://sites.google.com/view/dynmutr/home
Publication Agreement: pdf
Poster Spotlight Video: mp4
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