Datasets:
The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
ep_count: int64
ep_steps: int64
total_steps: int64
packages_lost: int64
ep_reward: double
speed_mean: double
speed_max: double
BestLap: double
LapNo_0: double
vs
ep_count: int64
ep_steps: int64
total_steps: int64
packages_lost: int64
ep_reward: double
speed_mean: double
speed_max: double
BestLap: double
LapNo_0: double
LapNo_1: double
LapNo_2: double
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 4072, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2404, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2597, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2102, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2124, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 523, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
ep_count: int64
ep_steps: int64
total_steps: int64
packages_lost: int64
ep_reward: double
speed_mean: double
speed_max: double
BestLap: double
LapNo_0: double
vs
ep_count: int64
ep_steps: int64
total_steps: int64
packages_lost: int64
ep_reward: double
speed_mean: double
speed_max: double
BestLap: double
LapNo_0: double
LapNo_1: double
LapNo_2: doubleNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for Assetto Corsa Gym
Dataset Summary
The AssettoCorsaGym dataset comprises 64 million steps, including 2.3 million steps from human drivers and the remaining from Soft Actor-Critic (SAC) policies. Data collection involved 15 drivers completing at least five laps per track and car. Participants included a professional e-sports driver, four experts, five casual drivers, and five beginners.
Supported Tasks and Leaderboards
- Autonomous driving
- Reinforcement learning
- Behavior cloning
- Imitation learning
Languages
English
Dataset Structure
See https://github.com/dasGringuen/assetto_corsa_gym/blob/main/data/paths.yml and https://github.com/dasGringuen/assetto_corsa_gym/blob/main/data/README.md
<track>
<car>
<human / policy>
laps
Data Instances
Each data instance includes telemetry data at 50Hz from a racing simulator, such as speed, position, acceleration, and control inputs (steering, throttle, brake).
Data Fields
Data Splits
We split the data in cars and tracks
Dataset Creation
Curation Rationale
The Assetto Corsa Gym dataset was curated to advance research in autonomous driving, reinforcement learning, and imitation learning. By providing a diverse dataset that includes both human driving data and data generated by Soft Actor-Critic (SAC) policies
Source Data
Initial Data Collection and Normalization
Data was collected from a racing simulator set up. Human drivers completed at least five laps per track and car, while SAC policies were trained from scratch and their replay buffers were recorded.
Who are the source language producers?
Human drivers of varying skill levels, including a professional e-sports driver, experts, casual drivers, and beginners.
Annotations
Annotation process
Data was automatically labeled during collection to differentiate between human and SAC policy data.
Who are the annotators?
The data was annotated by the research team at UC San Diego and Graz University of Technology.
Personal and Sensitive Information
The dataset does not contain any personally identifiable information. Drivers were anonymized and identified only by driver_id.
Considerations for Using the Data
Social Impact of Dataset
The dataset aims to contribute to the development of safer and more efficient autonomous driving systems by providing diverse driving data for training machine learning models.
Discussion of Biases
The dataset includes a wide range of driving skills, but there may still be biases based on the limited number of human participants and their specific driving styles. Additionally, the number of laps per track and car is unbalanced, which might affect the generalizability of models trained on this dataset. The selection of tracks and cars, as well as the specific conditions under which the data was collected, could also introduce biases that researchers should be aware of when using this dataset.
Other Known Limitations
- Limited number of tracks and cars
- Simulated driving environment may not fully capture real-world driving conditions
Additional Information
Dataset Curators
The dataset was curated by researchers at UC San Diego and Graz University of Technology.
Licensing Information
CC BY 4.0
Contributions
Thanks to @dasGringuen for adding this dataset.
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