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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)
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;
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()
A render() method, which renders the environment. Create the scene # The first step is to create a RoadNetwork that describes the geometry and topology of roads and lanes in the scene. This should be achieved in a YourEnv._make_road() method, called from YourEnv.reset() to set the self.road field.
30 de may. de 2023 · Project description. 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")
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()
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
