Authors: Moritz Harmel, Anubhav Paras, Andreas Pasternak, Gary Linscott

Published on: December 23, 2023

Impact Score: 8.45

Arxiv code: Arxiv:2312.15122

Summary

• What is new: Large-scale reinforcement learning experiments for autonomous driving that significantly outperform existing machine learning policies.
• Why this is important: Running reinforcement learning experiments for autonomous driving at necessary scales is difficult due to safety, scalability, and data collection issues.
• What the research proposes: An efficient and realistic driving simulator that leverages a large amount of real-world driving data.
• Results: The policy performance improves with scale, achieving a 64% reduction in failure rate and a 25% increase in driving progress rate compared to current state-of-the-art.

Technical Details

Technological frameworks used: Reinforcement learning, distributed computing.

Models used: Custom reinforcement learning models for autonomous driving.

Data used: Real-world driving data.

Potential Impact

Autonomous driving companies, automotive industry, driving simulation software market.

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