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Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Related Experiment Video

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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End-to-End Autonomous Driving Decision Method Based on Improved TD3 Algorithm in Complex Scenarios.

Tao Xu¹, Zhiwei Meng¹, Weike Lu²

¹National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130015, China.

Sensors (Basel, Switzerland)

|August 10, 2024

Summary

This study improves reinforcement learning for autonomous driving by addressing target network underestimation. The enhanced algorithm shows faster convergence and better stability in complex driving scenarios.

Keywords:

autonomous driving complex scenarios intelligent decision-making multiple critics reinforcement learning

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Area of Science:

Intelligent Automotive Systems
Machine Learning
Reinforcement Learning

Background:

Intelligent automotive systems require robust decision-making in complex scenarios.
Traditional methods struggle with complex driving environments.
Reinforcement learning offers superior decision-making but faces estimation inaccuracies.

Purpose of the Study:

To address the underestimation phenomenon in reinforcement learning for autonomous driving.
To propose an end-to-end decision-making method overcoming TD3 algorithm limitations.
To enhance the accuracy and stability of autonomous driving policies.

Main Methods:

An improved Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm was developed.
A triple-critic structure with target maximization was introduced to solve underestimation.
Multi-timestep averaging was employed to stabilize the policy.

Main Results:

The proposed method effectively solved the underestimation problem.
The algorithm demonstrated superior convergence speed compared to baseline methods.
Improved estimation accuracy and policy stability were achieved in simulations.

Conclusions:

The enhanced TD3 algorithm provides a more accurate and stable decision-making framework for autonomous driving.
The method shows significant potential for real-world intelligent automotive applications.
The triple-critic structure and multi-timestep averaging are effective solutions for TD3 limitations.