Francis Powlesland & Elena Kotljarova
On the straight and narrow path - How to get cars to drive themselves using reinforcement learning and trajectory optimization
#1about 5 minutes
A novel approach to self-driving cars
This project uses reinforcement learning to enable a car to learn on-the-drive, unlike pre-trained models that rely on static data.
#2about 3 minutes
Setting a baseline with a human driver
A human driver completes three laps on the physical racetrack to establish a benchmark time for the AI to compete against.
#3about 5 minutes
Observing the AI learn across 1500 laps
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.
#4about 6 minutes
Understanding the core concepts of reinforcement learning
A recap of the demo's results leads into an explanation of reinforcement learning's core ideas like agents, environments, actions, and maximizing rewards.
#5about 5 minutes
Applying Q-learning with states, actions, and Q-tables
Q-learning uses a table of states and actions to store learned values, making it easy to inspect and update the agent's knowledge.
#6about 4 minutes
Key parameters for tuning the Q-learning algorithm
The algorithm's behavior is controlled by key parameters like the learning rate (alpha), discount factor, and the exploration factor (epsilon).
#7about 1 minute
The technical architecture of the race track demo
The demo integrates PS4 controllers, an Arduino, the Watson IoT platform, a Node.js backend, and a React.js frontend.
#8about 3 minutes
Real-world application with Thyssen Krupp
A collaboration with Thyssen Krupp applies these reinforcement learning concepts to a full-size vehicle to learn and adapt its driving style.
#9about 7 minutes
Q&A on data, constraints, and local optima
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.
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