Summary of Torchdriveenv: a Reinforcement Learning Benchmark For Autonomous Driving with Reactive, Realistic, and Diverse Non-playable Characters, by Jonathan Wilder Lavington et al.
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
by Jonathan Wilder Lavington, Ke Zhang, Vasileios Lioutas, Matthew Niedoba, Yunpeng Liu, Dylan Green, Saeid Naderiparizi, Xiaoxuan Liang, Setareh Dabiri, Adam Ścibior, Berend Zwartsenberg, Frank Wood
First submitted to arxiv on: 7 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Multiagent Systems (cs.MA); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces TorchDriveSim and its benchmark extension TorchDriveEnv, a lightweight Python-based reinforcement learning benchmark for simulating autonomous vehicles. This simulator is designed to be efficient, easy to use, and modify, allowing users to test various factors influencing learned vehicle behavior. Unlike replay-based approaches, TorchDriveEnv is integrated with a state-of-the-art behavioral simulation API, enabling training and evaluation of driving models alongside data-driven Non-Playable Characters (NPCs). The authors demonstrate the efficiency and simplicity of TorchDriveEnv by evaluating common reinforcement learning baselines in both training and validation environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new simulator for autonomous vehicles that makes it easy to test different scenarios. This simulator is designed to be efficient, easy to use, and can be modified to test different things like different types of traffic or different ways cars move. The authors also compare their simulator to others that just replay data from the past, showing that theirs is better because it’s more realistic. Overall, this simulator makes it easier for people to develop autonomous vehicles by providing a more realistic way to test and train them. |
Keywords
» Artificial intelligence » Reinforcement learning
