Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning

TL;DR

This paper introduces a hierarchical reinforcement learning approach with a novel state-action abstraction for autonomous multi-lane cruising, enabling modularity and transferability from simulation to real-world scenarios.

Contribution

It presents a hierarchical framework with a new state-action abstraction that improves modularity and transferability in multi-lane cruising tasks.

Findings

Effective transfer of trained models from simple to complex dynamics.

Modular design facilitates extension of motion planning.

Hierarchical approach improves decision-making in multi-lane scenarios.

Abstract

Competent multi-lane cruising requires using lane changes and within-lane maneuvers to achieve good speed and maintain safety. This paper proposes a design for autonomous multi-lane cruising by combining a hierarchical reinforcement learning framework with a novel state-action space abstraction. While the proposed solution follows the classical hierarchy of behavior decision, motion planning and control, it introduces a key intermediate abstraction within the motion planner to discretize the state-action space according to high level behavioral decisions. We argue that this design allows principled modular extension of motion planning, in contrast to using either monolithic behavior cloning or a large set of hand-written rules. Moreover, we demonstrate that our state-action space abstraction allows transferring of the trained models without retraining from a simulated environment with virtually no dynamics to one with significantly more realistic dynamics. Together, these results suggest that our proposed hierarchical architecture is a promising way to allow reinforcement learning to be applied to complex multi-lane cruising in the real world.