Resultado de búsqueda
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;
- Highway - highway-env Documentation - The Farama Foundation
HighwayEnv (config: dict | None = None, render_mode: str |...
- Highway - highway-env Documentation - The Farama Foundation
In this task, the ego-vehicle starts on a main highway but soon approaches a road junction with incoming vehicles on the access ramp. The agent's objective is now to maintain a high speed while making room for the vehicles so that they can safely merge in the traffic.
Several scripts and notebooks to train driving policies on highway-env are available on this page. Here are a few of them: Highway with image observations and a CNN model Train SB3’s DQN on highway-fast-v0, but using image observations and a CNN model for the value function.
Renderers. The renderers work like capsules where all the Javascript related to a single page is put. Each page can have its custom renderer which will extend the built-in and default Highway.Renderer. A page that doesn't need specific Javascript doesn't need a renderer as well so the built-in Highway.Renderer will be used instead.
30 de may. de 2023 · Highway. env = gymnasium.make("highway-v0") In this task, the ego-vehicle is driving on a multilane highway populated with other vehicles. The agent's objective is to reach a high speed while avoiding collisions with neighbouring vehicles. Driving on the right side of the road is also rewarded.
Trajectory Planning on Highway. Plan a trajectory on highway-v0 using the OPD [HM08] implementation from rl-agents. Parking with Hindsight Experience Replay. Train a goal-conditioned parking-v0 policy using the [AWR+17] implementation from stable-baselines.
