Competitor-aware Race Management for Electric Endurance Racing
This addresses the problem of optimizing race management for electric endurance racing teams, but it is incremental as it builds on existing methods in game theory and reinforcement learning.
The paper tackled the problem of determining race-winning policies in electric endurance racing, which involves severe energy constraints and aerodynamic interactions, by proposing a bi-level framework combining game-theoretic optimal control and reinforcement learning; the results in a simulated 45-lap race showed that exploiting aerodynamic interactions is decisive for race outcome, with strategies differing from single-agent approaches.
Electric endurance racing is characterized by severe energy constraints and strong aerodynamic interactions. Determining race-winning policies therefore becomes a fundamentally multi-agent, game-theoretic problem. These policies must jointly govern low-level driver inputs as well as high-level strategic decisions, including energy management and charging. This paper proposes a bi-level framework for competitor-aware race management that combines game-theoretic optimal control with reinforcement learning. At the lower level, a multi-agent game-theoretic optimal control problem is solved to capture aerodynamic effects and asymmetric collision-avoidance constraints inspired by motorsport rules. Using this single-lap problem as the environment, reinforcement learning agents are trained to allocate battery energy and schedule pit stops over an entire race. The framework is demonstrated in a two-agent, 45-lap simulated race. The results show that effective exploitation of aerodynamic interactions is decisive for race outcome, with strategies that prioritize finishing position differing fundamentally from single-agent, minimum-time approaches.