This project uses reinforcement learning to enable a car to learn on-the-drive, unlike pre-trained models that rely on static data.
A human driver completes three laps on the physical racetrack to establish a benchmark time for the AI to compete against.
The AI's driving behavior evolves from random and unstable after 15 laps to smooth and optimized after 150, showing diminishing returns by 1500 laps.
A recap of the demo's results leads into an explanation of reinforcement learning's core ideas like agents, environments, actions, and maximizing rewards.
Q-learning uses a table of states and actions to store learned values, making it easy to inspect and update the agent's knowledge.
The algorithm's behavior is controlled by key parameters like the learning rate (alpha), discount factor, and the exploration factor (epsilon).
The demo integrates PS4 controllers, an Arduino, the Watson IoT platform, a Node.js backend, and a React.js frontend.
A collaboration with Thyssen Krupp applies these reinforcement learning concepts to a full-size vehicle to learn and adapt its driving style.
The speakers answer audience questions about the importance of data quality, how the car stays on the track, and how the algorithm avoids local optima.
38:12Daniel Graff & Andreas Wittmann
30:57Teresa Conceicao
26:55Jan Zawadzki
24:07Mike Butcher, Jürgen Müller, Katrin Lehmann & Tobias Regenfuss
19:24Liang Yu
48:01Jan Zawadzki
31:23Sayed Bouzouraa
32:26Krzysztof Cieślak
Jobs that call for the skills explored in this talk.
Warmwind
Waymo
London, United Kingdom
Jack & Jill\u002FExternal ATS
Everllence SE
Warmwind