A Simple Example for Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env

Related tags

Deep Learningtensorflowdaggertorcstensorflow-tutorialstensorlayerimitation-learninggym-torcstorcs-env
Overview

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

  1. Ubuntu (I only test on this)
  2. Python 3
  3. TensorLayer and TensorFlow
  4. 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:

  • 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, 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 !

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Comments
  • About the convergence and overfit

    About the convergence and overfit

    Hi, thanks for your job and I rewrite it using Keras in the attitude of learning. And I use your recommended hyper-parameters but when I run my program it's apt to overfit. Later on, I change the hyper-parameters , add BN and explicit initialization function of each layer. But it's still overfitting and the car runs 700 steps at the best time but still can't go through the all track. I have spent more than two weeks to tune it. I'm so confused of the tuning, why the same hyper-parameters can't achieve the same result? Why the network is so apt to overfit? For convenience, I update my programmer imitationLearning.py Can you give me some idea? Than you in advance.

    opened by marooncn 0
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