From the 1 of 5 linked papers with an AI index.
5 papers
Game Theory in Formula 1: From Physical to Strategic Interactions
Giona Fieni, Marc-Philippe Neumann, Francesca Furia +4
The paper proposes an optimization framework that uses game‑theoretic models (Nash and Stackelberg) to capture physical and strategic interactions in multi‑agent Formula 1 racing,…
Learning-based Multi-agent Race Strategies in Formula 1
Giona Fieni, Joschua Wüthrich, Marc-Philippe Neumann +1
In Formula 1, race strategies are adapted according to evolving race conditions and competitors' actions. This paper proposes a reinforcement learning approach for multi-agent race…
Bridging RL and MPC for mixed-integer optimal control with application to Formula 1 race strategies
Joschua Wüthrich, Romir Damle, Giona Fieni +3
We propose a hybrid reinforcement learning (RL) and model predictive control (MPC) framework for mixed-integer optimal control, where discrete variables enter the cost and dynamics…
Towards Learning-Based Formula 1 Race Strategies
Giona Fieni, Joschua Wüthrich, Marc-Philippe Neumann +2
This paper presents two complementary frameworks to optimize Formula 1 race strategies, jointly accounting for energy allocation, tire wear and pit stop timing. First, the race sce…
Game-theoretic Energy Management Strategies With Interacting Agents in Formula 1
Giona Fieni, Marc-Philippe Neumann, Alessandro Zanardi +2
This paper presents an interaction-aware energy management optimization framework for Formula 1 racing. The considered scenario involves two agents and a drag reduction model. Stra…