ULTRA

Unprotected Left Turn for Robust Agents

Ultra provides a gym-based environment using SMARTS for tackling intersection navigation and more specifically unprotected left turn.

Here is the summary of key features:

• Customizable scenarios with different levels of difficulty.
• Analysis tools to evaluate traffic designs including low-mid-high densities.
• Tools to support analyzing social-vehicle behaviors.
• Configurable train and test parameters.
• Benchmark results to train and test custom RL algorithms against.

Index

Get started by choosing an option below.

• Flowchart of ULTRA's interface with SMARTS
• Setting up ULTRA
• Train and Evaluate a Baseline Agent
• Create a Custom Agent
• More about ULTRA Agents
• ULTRA supports RLlib
• Multi-agent Experiments in ULTRA

Citing ULTRA

For a longer introduction to ULTRA, including its purpose, concepts, and benchmarks, please see ULTRA: A reinforcement learning generatlization benchmark for autonomous driving.

If you use ULTRA in your research, you can use to the following citation.

@misc{elsayed20_ultra,
  author          = {Elsayed, Mohamed and Hassanzadeh, Kimia and Nguyen, Nhat M,
                  and Alban, Montgomery and Zhu, Xiru and Graves, Daniel and
                  Luo, Jun},
  journal         = {Machine Learning for Autonomous Driving Workshoo, Neural
                  Information Processing Systems},
  title           = {ULTRA: A reinforcement learning generalization benchmark
                  for autonomous driving},
  url             = {https://ml4ad.github.io/files/papers2020/ULTRA: A
                  reinforcement learning generalization benchmark for autonomous
                  driving.pdf},
  year            = 2020,
}