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

Solving this task is still an open problem and it seems complicated to handle such difficult and highly variable situations with classic rules-based approach. This is why a significant part of the state of the art in autonomous driving [20, 4, 5] focuses on end-to-end systems, i.e. learning driving policy from data without relying on handcrafted rules. Imitation learning (IL) [28] aims to reproduce the behavior of an expert (a human driver for autonomous driving) by learning to mimic the control the human driver applied in the same situation. This leverages the massive amount of data annotated with human driving that most of automotive manufacturer and supplier can obtain relatively easily. On the other side, as the human driver is always in an almost perfect situation, IL algorithms suffer from a distribution mismatch, i.e. the algorithm will never encounter failing cases and thus will not react appropriately in those conditions.