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# output

This model is a fine-tuned version of [](https://huggingface.co/) on the city_learn dataset.

## Model description

state_mean = np.array(
[6.51944444e+00, 3.98379630e+00, 1.25000000e+01, 1.67850000e+01, 1.67849190e+01, 1.67851968e+01, 1.67854977e+01, 7.28990741e+01, 7.29056713e+01, 7.29093750e+01, 7.29134259e+01, 2.07319097e+02, 2.07319097e+02, 2.07185417e+02, 2.07236111e+02, 2.01118634e+02, 2.01118634e+02, 2.00806481e+02, 2.00887616e+02, 1.56366486e-01, 1.05916886e+00, 6.96371636e-01, 2.91179937e-01, 3.99157702e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01])

state_std = np.array(
[3.47125753e+00, 2.00155513e+00, 6.92218755e+00, 3.55389420e+00, 3.55381195e+00, 3.55403913e+00, 3.55461251e+00, 1.65420140e+01, 1.65465337e+01, 1.65478974e+01, 1.65489647e+01, 2.91883900e+02, 2.91883900e+02, 2.91755278e+02, 2.91833913e+02, 2.96415007e+02, 2.96415007e+02, 2.96260649e+02, 2.96305327e+02, 3.53750260e-02, 8.83521126e-01, 1.01549677e+00, 3.23319869e-01, 9.20646312e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-01])


## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 360

### Training results



### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["city_learn"], "model-index": [{"name": "output", "results": []}]}
transformers pytorch tensorboard decision_transformer generated_from_trainer dataset:city_learn endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #decision_transformer #generated_from_trainer #dataset-city_learn #endpoints_compatible #region-us 




# output

This model is a fine-tuned version of [](URL on the city_learn dataset.

## Model description

state_mean = URL(
[6.51944444e+00, 3.98379630e+00, 1.25000000e+01, 1.67850000e+01, 1.67849190e+01, 1.67851968e+01, 1.67854977e+01, 7.28990741e+01, 7.29056713e+01, 7.29093750e+01, 7.29134259e+01, 2.07319097e+02, 2.07319097e+02, 2.07185417e+02, 2.07236111e+02, 2.01118634e+02, 2.01118634e+02, 2.00806481e+02, 2.00887616e+02, 1.56366486e-01, 1.05916886e+00, 6.96371636e-01, 2.91179937e-01, 3.99157702e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01])

state_std = URL(
[3.47125753e+00, 2.00155513e+00, 6.92218755e+00, 3.55389420e+00, 3.55381195e+00, 3.55403913e+00, 3.55461251e+00, 1.65420140e+01, 1.65465337e+01, 1.65478974e+01, 1.65489647e+01, 2.91883900e+02, 2.91883900e+02, 2.91755278e+02, 2.91833913e+02, 2.96415007e+02, 2.96415007e+02, 2.96260649e+02, 2.96305327e+02, 3.53750260e-02, 8.83521126e-01, 1.01549677e+00, 3.23319869e-01, 9.20646312e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-01])


## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 360

### Training results



### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

# output

This model is a fine-tuned version of [](URL on the city_learn dataset. ## Model description

state_mean = URL(
[6.51944444e+00, 3.98379630e+00, 1.25000000e+01, 1.67850000e+01, 1.67849190e+01, 1.67851968e+01, 1.67854977e+01, 7.28990741e+01, 7.29056713e+01, 7.29093750e+01, 7.29134259e+01, 2.07319097e+02, 2.07319097e+02, 2.07185417e+02, 2.07236111e+02, 2.01118634e+02, 2.01118634e+02, 2.00806481e+02, 2.00887616e+02, 1.56366486e-01, 1.05916886e+00, 6.96371636e-01, 2.91179937e-01, 3.99157702e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01])

state_std = URL(
[3.47125753e+00, 2.00155513e+00, 6.92218755e+00, 3.55389420e+00, 3.55381195e+00, 3.55403913e+00, 3.55461251e+00, 1.65420140e+01, 1.65465337e+01, 1.65478974e+01, 1.65489647e+01, 2.91883900e+02, 2.91883900e+02, 2.91755278e+02, 2.91833913e+02, 2.96415007e+02, 2.96415007e+02, 2.96260649e+02, 2.96305327e+02, 3.53750260e-02, 8.83521126e-01, 1.01549677e+00, 3.23319869e-01, 9.20646312e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-01]) ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 360 ### Training results ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #decision_transformer #generated_from_trainer #dataset-city_learn #endpoints_compatible #region-us 
 # output

This model is a fine-tuned version of [](URL on the city_learn dataset. ## Model description

state_mean = URL(
[6.51944444e+00, 3.98379630e+00, 1.25000000e+01, 1.67850000e+01, 1.67849190e+01, 1.67851968e+01, 1.67854977e+01, 7.28990741e+01, 7.29056713e+01, 7.29093750e+01, 7.29134259e+01, 2.07319097e+02, 2.07319097e+02, 2.07185417e+02, 2.07236111e+02, 2.01118634e+02, 2.01118634e+02, 2.00806481e+02, 2.00887616e+02, 1.56366486e-01, 1.05916886e+00, 6.96371636e-01, 2.91179937e-01, 3.99157702e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01])

state_std = URL(
[3.47125753e+00, 2.00155513e+00, 6.92218755e+00, 3.55389420e+00, 3.55381195e+00, 3.55403913e+00, 3.55461251e+00, 1.65420140e+01, 1.65465337e+01, 1.65478974e+01, 1.65489647e+01, 2.91883900e+02, 2.91883900e+02, 2.91755278e+02, 2.91833913e+02, 2.96415007e+02, 2.96415007e+02, 2.96260649e+02, 2.96305327e+02, 3.53750260e-02, 8.83521126e-01, 1.01549677e+00, 3.23319869e-01, 9.20646312e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-01]) ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 360 ### Training results ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #decision_transformer #generated_from_trainer #dataset-city_learn #endpoints_compatible #region-us 
# output

This model is a fine-tuned version of [](URL on the city_learn dataset.## Model description

state_mean = URL(
[6.51944444e+00, 3.98379630e+00, 1.25000000e+01, 1.67850000e+01, 1.67849190e+01, 1.67851968e+01, 1.67854977e+01, 7.28990741e+01, 7.29056713e+01, 7.29093750e+01, 7.29134259e+01, 2.07319097e+02, 2.07319097e+02, 2.07185417e+02, 2.07236111e+02, 2.01118634e+02, 2.01118634e+02, 2.00806481e+02, 2.00887616e+02, 1.56366486e-01, 1.05916886e+00, 6.96371636e-01, 2.91179937e-01, 3.99157702e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01])

state_std = URL(
[3.47125753e+00, 2.00155513e+00, 6.92218755e+00, 3.55389420e+00, 3.55381195e+00, 3.55403913e+00, 3.55461251e+00, 1.65420140e+01, 1.65465337e+01, 1.65478974e+01, 1.65489647e+01, 2.91883900e+02, 2.91883900e+02, 2.91755278e+02, 2.91833913e+02, 2.96415007e+02, 2.96415007e+02, 2.96260649e+02, 2.96305327e+02, 3.53750260e-02, 8.83521126e-01, 1.01549677e+00, 3.23319869e-01, 9.20646312e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-01])## Intended uses & limitations

More information needed## Training and evaluation data

More information needed## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 360### Training results### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2