Roadmap for D4RL #175
Description
Recently, the Farama Foundation has recently taken over D4RL and has several plans for maintaining D4RL.
- Add a PyPI release
- Remove environments of necessary imports and have D4RL as a more modular project that supports a wider range of projects
- New core API (this is experimental so major changes might occur)
def load(dataset_name: str) -> OfflineDataset:
"""Load a D4RL dataset.
This function should return a handle to an offline RL dataset.
This is the most important entrypoint in the API. We should make
sure that this method returns everything needed for doing offline RL research
with D4RL datasets.
Args:
dataset_name: Name of the dataset.
"""
# Download the dataset if necessary.
def make(name_or_config: Union[str, EnvironmentConfig]) -> gym.Env:
"""Create an environment used in generating the dataset.
This function should produce an environment in which the dataset
is generated. The spec from the environment should match those
in the corresponding offline dataset. e.g., if there are frame-stacking
or action-repeat in the datasets, so should the recreated environments respect
those.
Dependencies to third-party libraries should only be lazy-loaded in this function,
and we should warn and error users if those dependencies are not met.
We can also put this function as an instance method on OfflineDataset.
Args:
name_or_config: the name of the dataset or an EnvironmentConfig
"""For the OfflineDataset, we plan to make a more framework-agnostic than RLDS that requires the use of Tensorflow Datasets.
In addition, we will be regenerating the dataset for all of the supported environments (e.g. we are dropping pybullet support due to mujoco being open source now) and confirming the performance reported in the paper with the new datasets.
If there are any more changes that would be of interest to users, please comment them below
If developers are interested in helping improve the project, please join the discord (https://discord.gg/nHg2JRN489) and message in the D4RL channel