Implemented papers

This folder contains subfolders where in each one we try to reproduce the experiments and results obtained in the literature using the PyRoboLearn framework.

Each subfolder contains

• a README file that:
  • summarizes the paper and provides the BibTex reference with a link to the original paper
  • states the possible differences between their experiment setup and ours and the obtained results
  • provides the current status (if it has been implemented yet, if the reproduction was successful or not), and describes what needs to be improved.
  • describes which variables the user can play with
• an environment (similar to gym environments) in the env.py file (that initializes the world, robots, rewards, states, actions, and so on) which allows to be used in other codes.
• a main file (main.py) that initializes the learning task (environment, policy, and so on), and provides an example on how to use the environment defined in env.py.
• a possible figures directory containing figures; this can include a picture of the environment, training plots, etc.
• a possible meshes directory containing meshes for the different objects that are present in the environment.
• a possible data folder containing the parameters/hyperparameters of a pre-trained learning model, or collected data that can be used for imitation learning for instance.

Structure

The name of each folder is <author_last_name><year><first_non_stop_word_in_title>, this is the same as returned by Google Scholar, when clicking on the BibTex button:

@article{<1st author's last name><year><1st non stop word in title>,
  title={<title>},
  author={<1st author's last name>, 1st author's first name and ...},
  journal={...},
  year={<year>}
}

Note: Stop words are commonly used words such as 'the', 'an', 'a', 'in', and others that search engines usually ignore.

Further notes

• It might be that in the future, this folder will be moved to its own repository, and each paper will have its own repository and will be loaded as submodules recursively (using git clone --recursive)
• I will often focus more on implementing the environments than training the various policies using the different algorithms
• Any comments, help, pull requests are appreciated.