Highway-Env is a collection of reinforcement learning environments for autonomous driving and tactical decision-making tasks.
The project provides simulated driving scenarios where agents learn to make decisions in traffic situations. It addresses the challenge of training autonomous driving systems by offering a set of configurable environments that model highway driving, merging, parking, and intersection scenarios. The environments are built on top of Gymnasium, the standard interface for reinforcement learning in Python, allowing agents to interact with these scenarios through standard RL training loops.
Developers should choose this tool when building and testing reinforcement learning agents for driving tasks, particularly for research into decision-making algorithms rather than perception systems. It suits projects that need quick iteration on tactical behavior without the computational overhead of full-scale simulators. The environments are lightweight and designed to run efficiently on standard hardware, making them appropriate for algorithm development and benchmarking before deployment to more complex simulators.
The project maintains active development with regular updates to its environments and features. The codebase receives consistent improvements and bug fixes. The maintainers respond to issues and pull requests from the community. Documentation is kept current alongside code changes.
