Part 7/15:
- Accelerating progress: RL can rapidly tune vehicle responses, especially in critical situations such as pedestrian avoidance or complex merges, which are underrepresented in real data.
The Architecture and Process of Reinforcement Learning
Tesla’s implementation of RL involves simulated environments where the vehicle agent tests different trajectories:
The vehicle "runs" through scenarios, such as a pedestrian stepping behind a parked car or needing to merge into a tight space.
Each trajectory receives a score based on safety, smoothness, legal compliance, and passenger comfort.
The neural network then adjusts its parameters via backpropagation—a process of nudging weights to favor better outcomes.