Reinforcement Learning in agent-based modeling to reduce carbon emissions in transportation

This paper explores the integration of reinforcement learning (RL) into transportation simulations to explore system interventions to reduce greenhouse gas emissions. The study leverages the Behavior, Energy, Automation, and Mobility (BEAM) transportation simulation framework in conjunction with the Berkeley Integrated System for Transportation Optimization (BISTRO) for scenario development. The main objective is to determine optimal parameters for transportation simulations to increase public transport usage and reduce individual vehicle reliance. Initial experiments were conducted on a simplified transportation scenario, and results indicate that RL can effectively find system interventions that increase public transit usage and decrease transportation emissions.