https://github.com/zsdonghao/Imitation-Learning-Dagger-Torcs
A Simple Example for Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env
https://github.com/zsdonghao/Imitation-Learning-Dagger-Torcs
dagger gym-torcs imitation-learning tensorflow tensorflow-tutorials tensorlayer torcs torcs-env
Last synced: 18 days ago
JSON representation
A Simple Example for Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env
- Host: GitHub
- URL: https://github.com/zsdonghao/Imitation-Learning-Dagger-Torcs
- Owner: zsdonghao
- Created: 2017-08-10T17:33:43.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-08-10T19:42:20.000Z (over 7 years ago)
- Last Synced: 2025-03-21T09:11:24.263Z (24 days ago)
- Topics: dagger, gym-torcs, imitation-learning, tensorflow, tensorflow-tutorials, tensorlayer, torcs, torcs-env
- Language: Python
- Homepage: https://github.com/zsdonghao/tensorlayer
- Size: 13.7 KB
- Stars: 71
- Watchers: 3
- Forks: 28
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-tensorflow - DAGGER - For Playing [Gym Torcs](https://github.com/ugo-nama-kun/gym_torcs) (Models/Projects)
- awesome-tensorflow - DAGGER - For Playing [Gym Torcs](https://github.com/ugo-nama-kun/gym_torcs) (Models/Projects)
- fucking-awesome-tensorflow - DAGGER - For Playing <b><code> 411⭐</code></b> <b><code> 164🍴</code></b> [Gym Torcs](https://github.com/ugo-nama-kun/gym_torcs)) (Models/Projects)
README
## Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env
This repository implements a simple algorithm for imitation learning: [DAGGER](https://www.cs.cmu.edu/~sross1/publications/Ross-AIStats11-NoRegret.pdf).
In this example, the agent only learns to control the steer [-1, 1], the speed is computed
automatically in `gym_torcs.TorcsEnv`.
## Requirements
1. Ubuntu (I only test on this)
2. Python 3
3. TensorLayer and TensorFlow
4. [Gym-Torcs](https://github.com/ugo-nama-kun/gym_torcs)
## Setting Up
It is a little bit boring to set up the environment, but any incorrect configurations will lead to FAILURE.
After installing [Gym-Torcs](https://github.com/ugo-nama-kun/gym_torcs), please follow the instructions to confirm everything work well:
- Open a terminal:
- Run `sudo torcs -vision` to start a game
- `Race --> Practice --> Configure Race`: set the driver to `scr_server 1` instead of `player`
- Open Torcs server by selecting `Race --> Practice --> New Race`:
This should result that Torcs keeps a blue screen with several text information.
- Open another terminal:
- Run `python snakeoil3_gym.py` on another terminal, it will shows how the fake AI control the car.
- Press F2 to see the driver view.
- Set image size to 64x64x3:
- The model is trained on 64x64 RGB observation.
- Run `sudo torcs -vision` to start a game
- `Options --> Display --> select 64x64 --> Apply`
## Usage
Make sure everything above work well and then run:
- `python dagger.py`
It will start a Torcs server at the beginning of every episode, and terminate the server when the car crashs or the speed is too low.
Note that, the self-contained `gym_torcs.py` is modified from [Gym-Torcs](https://github.com/ugo-nama-kun/gym_torcs), you can try different settings (like default speed, terminated speed) by modifying it.
## Results
After Episode 1, the car crashes after 315 steps.
After Episode 3, the car does not crash anymore !!!
The number of steps and episodes might vary depending on the parameters initialization.
ENJOY !