highway-env
A collection of minimalist Gymnasium environments for autonomous driving decision-making and reinforcement learning research.
What is highway-env?
Highway-env is a Python library providing a collection of lightweight simulation environments for autonomous driving decision-making tasks. It enables researchers and developers to train and evaluate reinforcement learning agents in various traffic scenarios such as highway navigation, merging, parking, and intersection handling. The environments are designed to be minimalist and fast, facilitating rapid experimentation and benchmarking.
Reinforcement learning researchers, autonomous driving engineers, and AI practitioners who need a standardized, lightweight simulation environment for developing and testing decision-making algorithms.
Developers choose highway-env for its simplicity, Gymnasium compatibility, and focus on core decision-making challenges without the complexity of full-scale simulators. It provides a curated set of challenging driving scenarios optimized for fast iteration and integration with popular RL frameworks.
Overview
A minimalist environment for decision-making in autonomous driving
Use Cases
Best For
- Training reinforcement learning agents for autonomous vehicle control
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