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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

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Published on: August 15, 2020

Learning autonomous race driving with action mapping reinforcement learning.

Yuanda Wang¹, Xin Yuan¹, Changyin Sun²

¹School of Automation, Southeast University, Nanjing 210096, China.

ISA Transactions

|May 23, 2024

Summary

This summary is machine-generated.

This study introduces an action mapping reinforcement learning (AM-RL) method for autonomous race car control. The novel approach enhances performance and generalizes driving policies across varying friction conditions.

Keywords:

Action mapping Autonomous race driving Reinforcement learning Safety constraint

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Autonomous race driving demands operating vehicles at physical limits for optimal performance.
Limited tire-road friction introduces complex state-dependent input constraints.
Existing reinforcement learning (RL) methods struggle with these dynamic constraints.

Purpose of the Study:

To develop a novel reinforcement learning (RL) approach for autonomous race driving.
To address state-dependent input constraints imposed by tire-road friction.
To enhance the generalization capability of learned driving policies.

Main Methods:

A novel action mapping (AM) mechanism was integrated into a reinforcement learning (RL) framework.
A numerical approximation method was developed to implement the AM mechanism, handling complex friction dynamics.
The AM-RL approach was evaluated in a custom-built race simulator.

Main Results:

The proposed AM-RL approach significantly reduced lap times compared to conventional RL methods.
Superior success rates were achieved with the AM-RL approach.
Experimental validation confirmed the generalization capability of the driving policy across different friction conditions.

Conclusions:

The AM-RL approach effectively manages state-dependent input constraints in autonomous race driving.
This method offers improved performance and robustness to varying friction conditions.
The AM mechanism enhances the practical applicability of RL in challenging control scenarios.