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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 new columns ({'LapNo_1', 'LapNo_2'})

This happened while the csv dataset builder was generating data using

hf://datasets/dasgringuen/assettoCorsaGym/data_sets/ks_barcelona-layout_gp/dallara_f317/20240311_SAC/eval/best/ks_barcelona-layout_gp/dallara_f317/eval_summary.csv (at revision e57e0ebf04c5d5d554539c85d2c98cb9c7ee4e93)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              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
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1546
              to
              {'ep_count': Value(dtype='int64', id=None), 'ep_steps': Value(dtype='int64', id=None), 'total_steps': Value(dtype='int64', id=None), 'packages_lost': Value(dtype='int64', id=None), 'ep_reward': Value(dtype='float64', id=None), 'speed_mean': Value(dtype='float64', id=None), 'speed_max': Value(dtype='float64', id=None), 'BestLap': Value(dtype='float64', id=None), 'LapNo_0': Value(dtype='float64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1537, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1106, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 new columns ({'LapNo_1', 'LapNo_2'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/dasgringuen/assettoCorsaGym/data_sets/ks_barcelona-layout_gp/dallara_f317/20240311_SAC/eval/best/ks_barcelona-layout_gp/dallara_f317/eval_summary.csv (at revision e57e0ebf04c5d5d554539c85d2c98cb9c7ee4e93)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

ep_count
int64
ep_steps
int64
total_steps
int64
packages_lost
int64
ep_reward
float64
speed_mean
float64
speed_max
float64
BestLap
float64
LapNo_0
float64
LapNo_1
float64
LapNo_2
float64
steps
int64
currentTime
float64
done
int64
speed
float64
reward
float64
gap
float64
world_position_y
float64
world_position_x
float64
RPM
float64
steerAngle
float64
brakeStatus
float64
accStatus
float64
actualGear
int64
packetId
int64
velocity_x
float64
velocity_y
float64
velocity_z
float64
yaw
float64
roll
float64
angular_velocity_y
float64
angular_velocity_x
float64
LapCount
int64
LapDist
float64
going_backwards
float64
current_action_abs_0
float64
current_action_abs_1
float64
current_action_abs_2
float64
actions_0
float64
actions_1
float64
actions_2
float64
rl_point
int64
out_of_track
float64
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End of preview.

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

See: https://github.com/dasGringuen/assetto_corsa_gym/blob/main/assetto_corsa_gym/assetto-corsa-autonomous-racing-plugin/plugins/sensors_par/structures.py

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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