https://github.com/sisl/gail-driver
https://github.com/sisl/gail-driver
Last synced: 9 months ago
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- Host: GitHub
- URL: https://github.com/sisl/gail-driver
- Owner: sisl
- License: other
- Created: 2017-01-09T18:41:10.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2017-10-12T15:57:20.000Z (over 8 years ago)
- Last Synced: 2024-03-24T17:10:23.667Z (about 2 years ago)
- Language: Python
- Size: 4.67 MB
- Stars: 105
- Watchers: 10
- Forks: 47
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# gail-driver
Utilities and scripts used to perform experiments described in "[Imitating Driver Behavior with Generative Adversarial Networks](https://arxiv.org/abs/1701.06699)". Built on [rllab](https://github.com/openai/rllab) and source code for [generative adversarial imitation learning](https://github.com/openai/imitation.git).
Train a model from the command line by running:
```
python scripts/train_gail_model.py
```
An ego vehicle trained through Generative Adversarial Imitation Learning (blue) navigating a congested highway scene.
# Requirements
Julia 0.5
ForwardNets.jl ([nextgen branch](https://github.com/tawheeler/ForwardNets.jl/tree/nextgen))
AutomotiveDrivingModels.jl ([gail branch](https://github.com/akuefler/AutomotiveDrivingModels.jl))
Note: This repository is not up to date with recent changes to the following Julia packages. We recommend using the following commits of these packages:
[AutoViz.jl](https://github.com/sisl/autoviz.jl) (commit 274dd08)
[NGSIM.jl](https://github.com/sisl/NGSIM.jl) (commit f16d684)
# References
Jonathan Ho, Stefano Ermon. "[Generative Adversarial Imitation Learning](https://cs.stanford.edu/~ermon/papers/imitation_nips2016_main.pdf)". _Advances in Neural Information Processing Systems (NIPS), 2016_
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel. "[Benchmarking Deep Reinforcement Learning for Continuous Control](http://arxiv.org/abs/1604.06778)". _Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016._