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# decision_transformer_1

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

## Model description

state_mean = np.array([6.52472527e+00, 4.00000000e+00, 1.25000000e+01, 1.68241415e+01, 1.68242216e+01, 1.68249313e+01, 1.68268315e+01, 7.29934753e+01, 7.29969093e+01, 7.29977106e+01, 7.29979396e+01, 2.08098329e+02, 2.08098329e+02, 2.07998283e+02, 2.08040522e+02, 2.01204785e+02, 2.01204785e+02, 2.00978709e+02, 2.01073375e+02, 1.56447270e-01, 1.06496225e+00, 6.98845768e-01, 2.90539899e-01, 4.02466726e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01])
state_std = np.array([3.45249551e+00, 2.00000100e+00, 6.92218755e+00, 3.55839049e+00, 3.55843321e+00, 3.55972060e+00, 3.56299330e+00, 1.64936264e+01, 1.64957718e+01, 1.64978640e+01, 1.65000009e+01, 2.92600647e+02, 2.92600647e+02, 2.92543689e+02, 2.92592247e+02, 2.96262436e+02, 2.96262436e+02, 2.96151575e+02, 2.96175911e+02, 3.53418023e-02, 8.88195655e-01, 1.01691038e+00, 3.23315111e-01, 9.21189104e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-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: 500

### Training results



### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["city_learn"], "model-index": [{"name": "decision_transformer_1", "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 




# decision_transformer_1

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

## Model description

state_mean = URL([6.52472527e+00, 4.00000000e+00, 1.25000000e+01, 1.68241415e+01, 1.68242216e+01, 1.68249313e+01, 1.68268315e+01, 7.29934753e+01, 7.29969093e+01, 7.29977106e+01, 7.29979396e+01, 2.08098329e+02, 2.08098329e+02, 2.07998283e+02, 2.08040522e+02, 2.01204785e+02, 2.01204785e+02, 2.00978709e+02, 2.01073375e+02, 1.56447270e-01, 1.06496225e+00, 6.98845768e-01, 2.90539899e-01, 4.02466726e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01])
state_std = URL([3.45249551e+00, 2.00000100e+00, 6.92218755e+00, 3.55839049e+00, 3.55843321e+00, 3.55972060e+00, 3.56299330e+00, 1.64936264e+01, 1.64957718e+01, 1.64978640e+01, 1.65000009e+01, 2.92600647e+02, 2.92600647e+02, 2.92543689e+02, 2.92592247e+02, 2.96262436e+02, 2.96262436e+02, 2.96151575e+02, 2.96175911e+02, 3.53418023e-02, 8.88195655e-01, 1.01691038e+00, 3.23315111e-01, 9.21189104e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-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: 500

### Training results



### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

# decision_transformer_1

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

state_mean = URL([6.52472527e+00, 4.00000000e+00, 1.25000000e+01, 1.68241415e+01, 1.68242216e+01, 1.68249313e+01, 1.68268315e+01, 7.29934753e+01, 7.29969093e+01, 7.29977106e+01, 7.29979396e+01, 2.08098329e+02, 2.08098329e+02, 2.07998283e+02, 2.08040522e+02, 2.01204785e+02, 2.01204785e+02, 2.00978709e+02, 2.01073375e+02, 1.56447270e-01, 1.06496225e+00, 6.98845768e-01, 2.90539899e-01, 4.02466726e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01])
state_std = URL([3.45249551e+00, 2.00000100e+00, 6.92218755e+00, 3.55839049e+00, 3.55843321e+00, 3.55972060e+00, 3.56299330e+00, 1.64936264e+01, 1.64957718e+01, 1.64978640e+01, 1.65000009e+01, 2.92600647e+02, 2.92600647e+02, 2.92543689e+02, 2.92592247e+02, 2.96262436e+02, 2.96262436e+02, 2.96151575e+02, 2.96175911e+02, 3.53418023e-02, 8.88195655e-01, 1.01691038e+00, 3.23315111e-01, 9.21189104e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-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: 500 ### Training results ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #decision_transformer #generated_from_trainer #dataset-city_learn #endpoints_compatible #region-us 
 # decision_transformer_1

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

state_mean = URL([6.52472527e+00, 4.00000000e+00, 1.25000000e+01, 1.68241415e+01, 1.68242216e+01, 1.68249313e+01, 1.68268315e+01, 7.29934753e+01, 7.29969093e+01, 7.29977106e+01, 7.29979396e+01, 2.08098329e+02, 2.08098329e+02, 2.07998283e+02, 2.08040522e+02, 2.01204785e+02, 2.01204785e+02, 2.00978709e+02, 2.01073375e+02, 1.56447270e-01, 1.06496225e+00, 6.98845768e-01, 2.90539899e-01, 4.02466726e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01])
state_std = URL([3.45249551e+00, 2.00000100e+00, 6.92218755e+00, 3.55839049e+00, 3.55843321e+00, 3.55972060e+00, 3.56299330e+00, 1.64936264e+01, 1.64957718e+01, 1.64978640e+01, 1.65000009e+01, 2.92600647e+02, 2.92600647e+02, 2.92543689e+02, 2.92592247e+02, 2.96262436e+02, 2.96262436e+02, 2.96151575e+02, 2.96175911e+02, 3.53418023e-02, 8.88195655e-01, 1.01691038e+00, 3.23315111e-01, 9.21189104e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-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: 500 ### Training results ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #decision_transformer #generated_from_trainer #dataset-city_learn #endpoints_compatible #region-us 
# decision_transformer_1

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

state_mean = URL([6.52472527e+00, 4.00000000e+00, 1.25000000e+01, 1.68241415e+01, 1.68242216e+01, 1.68249313e+01, 1.68268315e+01, 7.29934753e+01, 7.29969093e+01, 7.29977106e+01, 7.29979396e+01, 2.08098329e+02, 2.08098329e+02, 2.07998283e+02, 2.08040522e+02, 2.01204785e+02, 2.01204785e+02, 2.00978709e+02, 2.01073375e+02, 1.56447270e-01, 1.06496225e+00, 6.98845768e-01, 2.90539899e-01, 4.02466726e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01])
state_std = URL([3.45249551e+00, 2.00000100e+00, 6.92218755e+00, 3.55839049e+00, 3.55843321e+00, 3.55972060e+00, 3.56299330e+00, 1.64936264e+01, 1.64957718e+01, 1.64978640e+01, 1.65000009e+01, 2.92600647e+02, 2.92600647e+02, 2.92543689e+02, 2.92592247e+02, 2.96262436e+02, 2.96262436e+02, 2.96151575e+02, 2.96175911e+02, 3.53418023e-02, 8.88195655e-01, 1.01691038e+00, 3.23315111e-01, 9.21189104e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-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: 500### Training results### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2