deepdrive
dogfight-sandbox-hg2
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| deepdrive | dogfight-sandbox-hg2 | |
|---|---|---|
| 1 | 1 | |
| 872 | 130 | |
| 0.2% | 1.5% | |
| 0.0 | 2.7 | |
| 7 months ago | about 1 year ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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deepdrive
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Is it possible to train a self driving car on google colab?
I've been trying for a while now and I started thinking it may not be possible. If anyone has managed to train a self-driving car simulator using openai gym on google colab(preferably), or on any remote server (AWS, GCP, ...) please let me know. So far, I tried carla, airsim, svl, deepdrive and they are all equally useless unless run locally with a gui. I'd really appreciate if someone suggests some way that actually can make it possible.
dogfight-sandbox-hg2
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Tracking very small objects (not very good but a decent start)
It would be fun if you could try your tracking system on a real-time 3D simulator : https://github.com/harfang3d/dogfight-sandbox-hg2
What are some alternatives?
carla - Open-source simulator for autonomous driving research.
RocketPy - Next generation High-Power Rocketry 6-DOF Trajectory Simulation
simulator - A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles
gltf - A crate for loading glTF 2.0
AirSim - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research
EyeTrackVR - Open Source and Affordable, Virtual Reality Eye Tracking Platform.
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
python-digital-twin - 3D Digital Twin of a Poppy Ergo Junior robot, in Python
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
Avolition2 - 3D action-RPG game for Linux/Windows/MacOS written in Python3 based on Panda3D engine
simglucose - A Type-1 Diabetes simulator implemented in Python for Reinforcement Learning purpose
nn_vis - A project for processing neural networks and rendering to gain insights on the architecture and parameters of a model through a decluttered representation.