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README.md

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# HighJax: Highway Driving environment for Reinforcement Learning research
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<p align="center">
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<img src="misc/videos/demo.webp" alt="HighJax PPO training demo"><br/>
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<img src="https://raw.githubusercontent.com/HumanCompatibleAI/HighJax/master/misc/videos/demo.webp" alt="HighJax PPO training demo"><br/>
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<em>PPO agent learning to drive on a 4-lane highway</em>
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</p>
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```
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<p align="center">
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<img src="misc/images/figure.png" alt="Octane figure output" width="428"><br/>
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<img src="https://raw.githubusercontent.com/HumanCompatibleAI/HighJax/master/misc/images/figure.png" alt="Octane figure output" width="428"><br/>
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</p>
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### Behavior crafting
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Octane includes a behavior explorer for defining measurable policy properties. While watching an episode, press `b` to capture a scenario — mark which actions you want (positive weight) or don't want (negative weight) at that traffic state. Name it, and Octane saves the behavior to `~/.highjax/behaviors/`. The next time you run `highjax-trainer train`, all discovered behaviors are evaluated every epoch and their scores are recorded as `behavior.{name}` columns in `epochia.parquet`.
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<p align="center">
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<img src="misc/images/behavior_tui.png" alt="Behavior crafting dialog in Octane" width="364"><br/>
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<img src="https://raw.githubusercontent.com/HumanCompatibleAI/HighJax/master/misc/images/behavior_tui.png" alt="Behavior crafting dialog in Octane" width="364"><br/>
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<em>Defining a behavior scenario in Octane</em>
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</p>
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pyproject.toml

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[build-system]
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requires = ["setuptools>=61.0"]
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requires = ["setuptools>=77.0"]
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build-backend = "setuptools.build_meta"
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[project]
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"gymnasium>=1.2.3",
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]
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[project.urls]
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Homepage = "https://github.com/HumanCompatibleAI/HighJax"
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Repository = "https://github.com/HumanCompatibleAI/HighJax"
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Documentation = "https://github.com/HumanCompatibleAI/HighJax/tree/master/docs"
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[project.optional-dependencies]
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trainer = [
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"optax>=0.2.6",

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