Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env
This repository implements a simple algorithm for imitation learning: DAGGER. In this example, the agent only learns to control the steer [-1, 1], the speed is computed automatically in gym_torcs.TorcsEnv
.
Requirements
Ubuntu (I only test on this)
Python 3
TensorLayer and TensorFlow
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, please follow the instructions to confirm everything work well:
Usage
Make sure everything above work well and then run:
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, 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 !