Most reinforcement learning (RL) and sequential decision making algorithms require an agent to generate training data through large amounts of interactions with their environment to achieve optimal performance. This is highly inefficient, especially when generating those interactions is difficult, such as collecting data with a real robot or by interacting with a human expert. This issue can be mitigated by reusing external sources of knowledge, for example, the RL Unplugged Atari dataset, which includes data of a synthetic agent playing Atari games.

However, there are very few of these datasets and a variety of tasks and ways of generating data in sequential decision making (e.g., expert data or noisy demonstrations, human or synthetic interactions, etc.), making it unrealistic and not even desirable for the whole community to work on a small number of representative datasets because these will never be representative enough. Moreover, some of these datasets are released in a form that only works with certain algorithms, which prevents researchers from reusing this data. For example, rather than including the sequence of interactions with the environment, some datasets provide a set of random interactions, making it impossible to reconstruct the temporal relation between them, while others are released in slightly different formats, which can introduce subtle bugs that are very difficult to identify.

In this context, we introduce Reinforcement Learning Datasets (RLDS), and release a suite of tools for recording, replaying, manipulating, annotating and sharing data for sequential decision making, including offline RL, learning from demonstrations, or imitation learning. RLDS makes it easy to share datasets without any loss of information (e.g., keeping the sequence of interactions instead of randomizing them) and to be agnostic to the underlying original format, enabling users to quickly test new algorithms on a wider range of tasks. Additionally, RLDS provides tools for collecting data generated by either synthetic agents (EnvLogger) or humans (RLDS Creator), as well as for inspecting and manipulating the collected data. Ultimately, integration with TensorFlow Datasets (TFDS) facilitates the sharing of RL datasets with the research community.

With RLDS, users can record interactions between an agent and an environment in a lossless and standard format. Then, they can use and transform this data to feed different RL or Sequential Decision Making algorithms, or to perform data analysis.

Dataset Structure
Algorithms in RL, offline RL, or imitation learning may consume data in very different formats, and, if the format of the dataset is unclear, it's easy to introduce bugs caused by misinterpretations of the underlying data. RLDS makes the data format explicit by defining the contents and the meaning of each of the fields of the dataset, and provides tools to re-align and transform this data to fit the format required by any algorithm implementation. In order to define the data format, RLDS takes advantage of the inherently standard structure of RL datasets — i.e., sequences (episodes) of interactions (steps) between agents and environments, where agents can be, for example, rule-based/automation controllers, formal planners, humans, animals, or a combination of these. Each of these steps contains the current observation, the action applied to the current observation, the reward obtained as a result of applying action, and the discount obtained together with reward. Steps also include additional information to indicate whether the step is the first or last of the episode, or if the observation corresponds to a terminal state. Each step and episode may also contain custom metadata that can be used to store environment-related or model-related data.

Producing the Data
Researchers produce datasets by recording the interactions with an environment made by any kind of agent. To maintain its usefulness, raw data is ideally stored in a lossless format by recording all the information that is produced, keeping the temporal relation between the data items (e.g., ordering of steps and episodes), and without making any assumption on how the dataset is going to be used in the future. For this, we release EnvLogger, a software library to log agent-environment interactions in an open format.

EnvLogger is an environment wrapper that records agent–environment interactions and saves them in long-term storage. Although EnvLogger is seamlessly integrated in the RLDS ecosystem, we designed it to be usable as a stand-alone library for greater modularity.

As in most machine learning settings, collecting human data for RL is a time consuming and labor intensive process. The common approach to address this is to use crowd-sourcing, which requires user-friendly access to environments that may be difficult to scale to large numbers of participants. Within the RLDS ecosystem, we release a web-based tool called RLDS Creator, which provides a universal interface to any human-controllable environment through a browser. Users can interact with the environments, e.g., play the Atari games online, and the interactions are recorded and stored such that they can be loaded back later using RLDS for analysis or to train agents.

Sharing the Data
Datasets are often onerous to produce, and sharing with the wider research community not only enables reproducibility of former experiments, but also accelerates research as it makes it easier to run and validate new algorithms on a range of scenarios. For that purpose, RLDS is integrated with TensorFlow Datasets (TFDS), an existing library for sharing datasets within the machine learning community. Once a dataset is part of TFDS, it is indexed in the global TFDS catalog, making it accessible to any researcher by using tfds.load(name_of_dataset), which loads the data either in Tensorflow or in Numpy formats.

TFDS is independent of the underlying format of the original dataset, so any existing dataset with RLDS-compatible format can be used with RLDS, even if it was not originally generated with EnvLogger or RLDS Creator. Also, with TFDS, users keep ownership and full control over their data and all datasets include a citation to credit the dataset authors.

Consuming the Data
Researchers can use the datasets in order to analyze, visualize or train a variety of machine learning algorithms, which, as noted above, may consume data in different formats than how it has been stored. For example, some algorithms, like R2D2 or R2D3, consume full episodes; others, like Behavioral Cloning or ValueDice, consume batches of randomized steps. To enable this, RLDS provides a library of transformations for RL scenarios. These transformations have been optimized, taking into account the nested structure of the RL datasets, and they include auto-batching to accelerate some of these operations. Using those optimized transformations, RLDS users have full flexibility to easily implement some high level functionalities, and the pipelines developed are reusable across RLDS datasets. Example transformations include statistics across the full dataset for selected step fields (or sub-fields) or flexible batching respecting episode boundaries. You can explore the existing transformations in this tutorial and see more complex real examples in this Colab.

Available Datasets
At the moment, the following datasets (compatible with RLDS) are in TFDS:

• a subset of D4RL with tasks from Mujoco and Adroit
• the RLUnplugged DMLab, Atari, and Real World RL datasets
• three Robosuite datasets generated with the RLDS tools

Our team is committed to quickly expanding this list in the near future and external contributions of new datasets to RLDS and TFDS are welcomed.

Conclusion
The RLDS ecosystem not only improves reproducibility of research in RL and sequential decision making problems, but also enables new research by making it easier to share and reuse data. We hope the capabilities offered by RLDS will initiate a trend of releasing structured RL datasets, holding all the information and covering a wider range of agents and tasks.

Acknowledgements
Besides the authors of this post, this work has been done by Google Research teams in Paris and Zurich in Collaboration with Deepmind. In particular by Sertan Girgin, Damien Vincent, Hanna Yakubovich, Daniel Kenji Toyama, Anita Gergely, Piotr Stanczyk, Raphaël Marinier, Jeremiah Harmsen, Olivier Pietquin and Nikola Momchev. We also want to thank the collaboration of other engineers and researchers who provided feedback and contributed to the project. In particular, George Tucker, Sergio Gomez, Jerry Li, Caglar Gulcehre, Pierre Ruyssen, Etienne Pot, Anton Raichuk, Gabriel Dulac-Arnold, Nino Vieillard, Matthieu Geist, Alexandra Faust, Eugene Brevdo, Tom Granger, Zhitao Gong, Toby Boyd and Tom Small.