Gene

Engineering

Aerospace engineering

Pure mathematics

Biology

Statistics

Computer security

Robustness (evolution)

Field (mathematics)

Transparency (behavior)

Biochemistry

Computer science

Genetics

Black box

Roundabout

Reinforcement learning

Mathematics

Error-driven learning

Artificial intelligence

Parametric statistics

Chemistry

Intersection (aeronautics)

Machine learning

Robotics and Autonomous Systems

The design of high-level decision-making systems is a topical problem in the field of autonomous driving. In this paper, we combine traditional rule-based strategies and reinforcement learning (RL) with the goal of achieving transparency and robustness. On the one hand, the use of handcrafted rule-based controllers allows for transparency, i.e., it is always possible to determine why a given decision was made, but they struggle to scale to complex driving scenarios, in which several objectives need to be considered. On the other hand, black-box RL approaches enable us to deal with more complex scenarios, but they are usually hardly interpretable. In this paper, we combine the best properties of these two worlds by designing parametric rule-based controllers, in which interpretable rules can be provided by domain experts and their parameters are learned via RL. After illustrating how to apply parameter-based RL methods (PGPE) to this setting, we present extensive numerical simulations in the highway and in two urban scenarios: intersection and roundabout. For each scenario, we show the formalization as an RL problem and we discuss the results of our approach in comparison with handcrafted rule-based controllers and black-box RL techniques.

Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving