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Release of sketchy
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Louise Deason
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# Sketchy data
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This is a dataset accompanying the paper
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[Scaling data-driven robotics with reward sketching and batch reinforcement learning](https://arxiv.org/abs/1909.12200).
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If you use this dataset in your research please cite
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```
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@article{cabi2019,
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title={Scaling data-driven robotics with reward sketching and batch reinforcement learning},
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author={Serkan Cabi and
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Sergio G{\'o}mez Colmenarejo and
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Alexander Novikov and
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Ksenia Konyushkova and
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Scott Reed and
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Rae Jeong and
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Konrad {\.Z}o\l{}na and
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Yusuf Aytar and
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David Budden and
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Mel Vecerik and
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Oleg Sushkov and
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David Barker and
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Jonathan Scholz and
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Misha Denil and
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Nando de Freitas and
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Ziyu Wang},
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journal={arXiv preprint arXiv:1909.12200},
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year={2019}
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}
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```
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## See example data
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There is a small amount of example data included in this repository. To examine
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it, run the following commands from the repository root (i.e. one level up from
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this folder):
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```
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python3 -m venv .sketchy_env
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source .sketchy_env/bin/activate
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pip install --upgrade pip
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pip install -r sketchy/requirements.txt
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python -m sketchy.dataset_example --show_images
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```
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For an example of loading rewards for episodes see `reward_example.py`.
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## Download the full dataset
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Run `./download.sh path/to/download/folder` to download the full dataset. The
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full dataset requires ~5.0TB of disk space to download, and extracts to approximately the same size.
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You can edit `download.sh` to download subsets of the data.
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Once the dataset has been downloaded it can be extracted wtih
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`./extract.sh path/to/download/folder`.
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### Named subsets
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We provide several named subsets of the full dataset, which can be easily
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downloaded on their own. See `download.sh` for a description of the subsets
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that are provided.
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The episodes in each of these named subsets are identified by a tag in the
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metadata.
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If you would like to curate your own subset you can download the metadata
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file and inspect the `ArchiveFiles` table (see below) to figure out which
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archive files contain the episodes you want.
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# Dataset Contents
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The dataset is distribted as a *metadata file* (`metadata.sqlite`) and a
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collection of *archive files* (with names ending in `.tar.bz2`).
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The metadata file contains information about the episodes, including annotated
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rewards for a subset of the episodes.
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Each archive file contains several *episode files*, which have names like
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`10000313341320364033_b615a417-ce34-41a8-8411-2a1ce3f3bd07`.
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Each episode file is a
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[tfrecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) file,
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containing a sequence of *timesteps* for a single episode.
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Each timestep is a `tf.train.Example` proto containing features corresponding to
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the observations and actions from a particular point in time.
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## Metadata
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The metadata file, `metadata.sqlite`, is a sqlite database containing metadata
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describing the contents of the files in the dataset.
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The following sections describe the important metadata tables. You can find the
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full schema by running
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```
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sqlite3 metadata.sqlite <<< .schema
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```
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### Episodes
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- `EpisodeId`: A string of digits that uniquely identifies the episode.
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- `TaskId``: A human readable name for the task corresponding to the behavior
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that generated the episode.
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- `DataPath`: The name of the episode file holding the data for this episode.
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- `EpisodeType`: A string describing the type of policy that generated the
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episode. Possible values are:
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- `EPISODE_ROBOT_AGENT`: The behavior policy is a learned or scripted
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controller.
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- `EPISODE_ROBOT_TELEOPERATION`: The behavior policy is a human teleoperating
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the robot.
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- `EPISODE_ROBOT_DAGGER`: The behavior policy is a mix of controller and human
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generated actions.
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- `Timestamp`: A unix timestamp recording when the episode was generated.
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### EpisodeTags
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- `EpisodeId`: Foreign key into the `Episodes` table.
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- `Tag`: A human readable identifier for some aspect of the episode (e.g. which
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object set is used).
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### RewardSequences
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- `EpisodeId`: Foreign key into the `Episodes` table.
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- `RewardSequenceId`: Distinguishes multiple rewards for the same episode.
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- `RewardTaskId`: A human readable name of the task for this reward signal.
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Typically the same as the corresponding `TaskId` in the `Episodes` table.
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- `Type`: A string describing the type of reward signal. Currently the only
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value is `REWARD_SKETCH`.
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- `Values`: A sequence of float32 values, packed as a binary blob. There is one
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float value for each frame of the episode, corresponding to the annotated
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reward.
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### ArchiveFiles
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- `EpisodeId`: Foreign key into the `Episodes` table.
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- `ArchiveFile`: Name of the archive file containing the corresponding episode.
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## Episodes
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Each episode file is a
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[tfrecords](https://www.tensorflow.org/tutorials/load_data/tfrecord) file
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containing a sequence of timesteps, encoded as
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[`tf.train.Example`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto)
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protos.
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Each episode file contains a single episode, and each timestep within an episode
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contains all of the observations and actions associated with a that timestep as
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a single `tf.train.Example`. Within each episode file the timesteps are
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temporally ordered, so reading a file from beginning to end will visit all of
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the timesteps from the episode in the order they occurred.
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Observations and actions occur at 10Hz.
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## Timesteps
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Each timestep is a collection of observations and actions. Actions stored with a
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timestep correspond to actions taken in response to the observations they are
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stored with.
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For a description of the shapes and types of the timestep data, see the data
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loader in `sketchy.py`.
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# Dataset Metadata
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The following table is necessary for this dataset to be indexed by search
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engines such as <a href="https://g.co/datasetsearch">Google Dataset Search</a>.
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<div itemscope itemtype="http://schema.org/Dataset">
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<table>
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<tr>
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<th>property</th>
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<th>value</th>
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</tr>
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<tr>
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<td>name</td>
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<td><code itemprop="name">Sketchy</code></td>
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</tr>
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<tr>
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<td>url</td>
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<td><code itemprop="url">https://github.com/deepmind/deepmind_research/sketchy</code></td>
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</tr>
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<tr>
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<td>sameAs</td>
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<td><code itemprop="sameAs">https://github.com/deepmind/deepmind_research/sketchy</code></td>
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</tr>
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<tr>
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<td>description</td>
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<td><code itemprop="description">
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Data accompanying
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[Scaling data-driven robotics with reward sketching and batch reinforcement learning](https://arxiv.org/abs/1909.12200).
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</code></td>
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</tr>
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<tr>
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<td>provider</td>
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<td>
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<div itemscope itemtype="http://schema.org/Organization" itemprop="provider">
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<table>
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<tr>
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<th>property</th>
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<th>value</th>
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</tr>
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<tr>
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<td>name</td>
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<td><code itemprop="name">DeepMind</code></td>
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</tr>
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<tr>
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<td>sameAs</td>
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<td><code itemprop="sameAs">https://en.wikipedia.org/wiki/DeepMind</code></td>
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</tr>
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</table>
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</div>
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</td>
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</tr>
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<tr>
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<td>citation</td>
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<td><code itemprop="citation">https://identifiers.org/arxiv:1909.12200</code></td>
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</tr>
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</table>
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</div>
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