NehalNevle / hgail

Worked on Hgail to integrate brake and also traffic and train the irl changed the data pipeline and intregated it with ROS

Hierarchical GAIL

Official implemention of the paper Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments.

Youtube Playlist hGAIL Supplementary Videos.

Installation

1. Install python3.8.
2. Install required packages from requirements.txt file.

Reproducing results

• To collect data from expert run the start_expert.sh script.

• To train the agents from the paper set the following variables global variables from the file rl_train.py.

• The variable FAKE_BIRDVIEW controls if the agent will use the birdview from the env or generated by the GAN.

• The variable GAIL controls if the agent will be trained using GAIL or PPO.

• The variable RGB_GAIL controls if the agent trains from birdview images or directly from images generated by the cameras.

• The variable RESUME_LAST_TRAIN controls if the agent will start a new training or resume the last one.

• To train an agent using GAIL or PPO run the start_rl.sh script after setting the appropriate variables.

• To train an agent using Behavior Cloning run the start_bc.sh script.

• To train only the GAN birdview using the samples from the expert demonstrations run the start_gan.sh script.

Results

• The results are logged using Wandb (Weights & Biases) that is similar to an online user based tensorboard.

• The training is logged in runs, where one csv table with data from every step is generated for each environment.

• The agent parameters are saved in a pytorch file every epoch in the ckpt directory identified by the epoch number of steps.

• To evaluate an agent run the eval_agent.sh script after setting the appropriate variables from in file evaluate_agent.py.

Generating plots

• In the paper_plots directory there are many scripts to generate the plots used in the paper

Generating videos

• To generate videos from the agent evaluation use the generate_video.py python script.

Disclaimer

This project is built upon the code from the following projects:

• Official code release of the paper
  End-to-End Urban Driving by Imitating a Reinforcement Learning Coach
  

• Official code release of the paper
  Augmenting GAIL with BC for sample efficient imitation learning
  

• PyTorch-GAN

Citation

Please consider citing our paper if you find it useful or want to use it in your research:

@misc{hGAIL,
      title={Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments}, 
      author={Gustavo Claudio Karl Couto and Eric Aislan Antonelo},
      year={2023},
}