How can Sutton's RL apply to autonomous driving?
Reinforcement Learning offers a powerful framework for autonomous driving. An agent (the car) can learn to navigate complex environments by interacting with a simulated or real world. The reward function could be designed to encourage safe driving, efficient routing, and adherence to traffic laws. Through trial and error, the agent would learn optimal driving policies, such as when to accelerate, brake, change lanes, and react to unexpected events, all aimed at maximizing a long-term reward signal representing a successful and safe journey.
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