TorchDriveEnv is a lightweight 2D driving reinforcement learning environment, supported by a solid simulator and smart non-playable characters
Project description
Installation
The basic installation of torchdriveenv uses an OpenCV renderer, which is slower but easy to install. PyTorch3D renderer can be faster, but it requires specific versions of CUDA and PyTorch, so it is best installed in Docker.
Opencv rendering
To install the “torchdriveenv” with opencv rendering:
pip install torchdriveenv
To run examples:
Set the $IAI_API_KEY and $WANDB_API_KEY
pip install torchdriveenv[baselines]
cd examples
python rl_training.py
Pytorch3d rendering
To install the “torchdriveenv” with Pytorch3d rendering:
docker build --target torchdriveenv-first-release -t torchdriveenv-first-release:latest .
To run examples:
Set the $IAI_API_KEY and $WANDB_API_KEY
cd examples
docker compose up rl-training
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file torchdriveenv-0.1.1.tar.gz.
File metadata
- Download URL: torchdriveenv-0.1.1.tar.gz
- Upload date:
- Size: 5.7 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ea00be0e073e479320a5ecaea4908851ab1686942c7d7a04ff0c6c00b5c19058
|
|
| MD5 |
86831c53c458f9c3cb47fd66e602efc3
|
|
| BLAKE2b-256 |
181430a006da240b3d7c1df1f349de2665d1d0ae39534ada7979dcc4a9430911
|
File details
Details for the file torchdriveenv-0.1.1-py3-none-any.whl.
File metadata
- Download URL: torchdriveenv-0.1.1-py3-none-any.whl
- Upload date:
- Size: 5.8 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
19219d8ee5db11fa2a72630c0cf1ba439da4d14e76f0e6308566126846e21d8f
|
|
| MD5 |
9d66301f77f2ded27410f48968135587
|
|
| BLAKE2b-256 |
118c1c00f43c2e689704a85b1099859ef7b46c27c4b5a1a5d631f2c3c9992215
|