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49 lines
2.3 KiB
Markdown
49 lines
2.3 KiB
Markdown
## Implemented papers
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This folder contains subfolders where in each one we try to reproduce the experiments and results obtained in the
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literature using the *PyRoboLearn* framework.
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Each subfolder contains
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- a `README` file that:
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- summarizes the paper and provides the BibTex reference with a link to the original paper
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- states the possible differences between their experiment setup and ours and the obtained results
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- provides the current status (if it has been implemented yet, if the reproduction was successful or not), and
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describes what needs to be improved.
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- describes which variables the user can play with
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- an environment (similar to gym environments) in the `env.py` file (that initializes the world, robots, rewards,
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states, actions, and so on) which allows to be used in other codes.
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- a main file (`main.py`) that initializes the learning task (environment, policy, and so on), and provides an
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example on how to use the environment defined in `env.py`.
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- a possible `figures` directory containing figures; this can include a picture of the environment, training plots,
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etc.
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- a possible `meshes` directory containing meshes for the different objects that are present in the environment.
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- a possible `data` folder containing the parameters/hyperparameters of a pre-trained learning model, or collected
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data that can be used for imitation learning for instance.
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### Structure
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The name of each folder is `<author_last_name><year><first_non_stop_word_in_title>`, this is the same as returned by
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[Google Scholar](https://scholar.google.com/), when clicking on the `BibTex` button:
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```
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@article{<1st author's last name><year><1st non stop word in title>,
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title={<title>},
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author={<1st author's last name>, 1st author's first name and ...},
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journal={...},
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year={<year>}
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}
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```
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*Note*: Stop words are commonly used words such as 'the', 'an', 'a', 'in', and others that search engines usually
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ignore.
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### Further notes
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- It might be that in the future, this folder will be moved to its own repository, and each paper will have its own
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repository and will be loaded as submodules recursively (using `git clone --recursive`)
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- I will often focus more on implementing the environments than training the various policies using the different
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algorithms
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- Any comments, help, pull requests are appreciated.
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