End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances

Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own experiments and not rule-based control methods. However, there is no RL algorithm yet capable of handling a task as difficult as urban driving... (read more)

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