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  1. Autonomous Highway Merging in Mixed Traffic Using Reinforcement Learning and Motion Predictive Safety Controller (Oct 2022) High-Level Decision-Making Non-player Vehicles (Nov 2022) Designing an Interpretability Analysis Framework for Deep Reinforcement Learning (DRL) Agents in Highway Automated Driving Simulation (Feb 2023)

  2. Welcome to highway-env’s documentation!. This project gathers a collection of environment for decision-making in Autonomous Driving.. The purpose of this documentation is to provide: a quick start guide describing the environments and their customization options;. a detailed description of the nuts and bolts of the project, and how you can contribute.. How to cite this work?

  3. Here is a quick example of how to create an environment: env = gym.make('highway-v0', render_mode='rgb_array') env.reset() for _ in range(3): action = env.action_type.actions_indexes["IDLE"] obs, reward, done, truncated, info = env.step(action) env.render() plt.imshow(env.render()) plt.show()

  4. HighwayEnv (config: dict | None = None, render_mode: str | None = None) [source] # A highway driving environment. The vehicle is driving on a straight highway with several lanes, and is rewarded for reaching a high speed, staying on the rightmost lanes and avoiding collisions.

  5. pypi.org › project › highway-envhighway-env · PyPI

    30 de may. de 2023 · highway-env. A collection of environments for autonomous driving and tactical decision-making tasks. An episode of one of the environments available in highway-env. Try it on Google Colab! The environments Highway env = gymnasium. make ("highway-v0") In this task, the ego-vehicle is driving on a multilane highway populated with other ...

  6. import gym import highway_env from matplotlib import pyplot as plt %matplotlib inline env = gym.make('highway-v0') env.reset() for _ in range(3): action = env.action_type.actions_indexes["IDLE"] obs, reward, done, info = env.step(action) env.render() plt.imshow(env.render(mode="rgb_array")) plt.show()

  7. render (mode: str = 'human') → Optional[numpy.ndarray] [source] ¶ Render the environment. Create a viewer if none exists, and use it to render an image. :param mode: the rendering mode

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