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# decision_transformer_2

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

## Model description

state_mean = np.array([ 6.519444444444445, 3.9837962962962963, 12.5, 16.78500002964779, 16.78491901104097, 16.785196788774595, 16.7854977140824, 72.89907407407408, 72.90567129629629, 72.909375, 72.91342592592592, 207.3190972222222, 207.3190972222222, 207.18541666666667, 207.23611111111111, 201.11863425925927, 201.11863425925927, 200.80648148148148, 200.88761574074073, 0.15636648599282596, 1.0591688615113237, 0.6963716355719771, 0.29117993655536184, 0.39915770157282743, 0.27310532142960087, 0.27310532142960087, 0.27310532142960087, 0.27310532142960087])

state_std = np.array([ 3.4712575323780417, 2.001555126539623, 6.922187552431729, 3.553894201620696, 3.553811950901204, 3.5540391337133648, 3.5546125114989646, 16.542013984734275, 16.546533736927085, 16.54789739417334, 16.548964694872215, 291.88390011273555, 291.8839001127356, 291.7552778101503, 291.83391263987227, 296.4150072818235, 296.4150072818235, 296.26064904211574, 296.30532672046087, 0.03537502595557663, 0.8835211260856368, 1.0154967670063433, 0.3233198693428126, 0.9206463117416827, 0.11787932777153287, 0.11787932777153277, 0.11787932777153269, 0.11787932777153291])



## 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_2", "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_2

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

## Model description

state_mean = URL([ 6.519444444444445, 3.9837962962962963, 12.5, 16.78500002964779, 16.78491901104097, 16.785196788774595, 16.7854977140824, 72.89907407407408, 72.90567129629629, 72.909375, 72.91342592592592, 207.3190972222222, 207.3190972222222, 207.18541666666667, 207.23611111111111, 201.11863425925927, 201.11863425925927, 200.80648148148148, 200.88761574074073, 0.15636648599282596, 1.0591688615113237, 0.6963716355719771, 0.29117993655536184, 0.39915770157282743, 0.27310532142960087, 0.27310532142960087, 0.27310532142960087, 0.27310532142960087])

state_std = URL([ 3.4712575323780417, 2.001555126539623, 6.922187552431729, 3.553894201620696, 3.553811950901204, 3.5540391337133648, 3.5546125114989646, 16.542013984734275, 16.546533736927085, 16.54789739417334, 16.548964694872215, 291.88390011273555, 291.8839001127356, 291.7552778101503, 291.83391263987227, 296.4150072818235, 296.4150072818235, 296.26064904211574, 296.30532672046087, 0.03537502595557663, 0.8835211260856368, 1.0154967670063433, 0.3233198693428126, 0.9206463117416827, 0.11787932777153287, 0.11787932777153277, 0.11787932777153269, 0.11787932777153291])



## 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_2

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

state_mean = URL([ 6.519444444444445, 3.9837962962962963, 12.5, 16.78500002964779, 16.78491901104097, 16.785196788774595, 16.7854977140824, 72.89907407407408, 72.90567129629629, 72.909375, 72.91342592592592, 207.3190972222222, 207.3190972222222, 207.18541666666667, 207.23611111111111, 201.11863425925927, 201.11863425925927, 200.80648148148148, 200.88761574074073, 0.15636648599282596, 1.0591688615113237, 0.6963716355719771, 0.29117993655536184, 0.39915770157282743, 0.27310532142960087, 0.27310532142960087, 0.27310532142960087, 0.27310532142960087])

state_std = URL([ 3.4712575323780417, 2.001555126539623, 6.922187552431729, 3.553894201620696, 3.553811950901204, 3.5540391337133648, 3.5546125114989646, 16.542013984734275, 16.546533736927085, 16.54789739417334, 16.548964694872215, 291.88390011273555, 291.8839001127356, 291.7552778101503, 291.83391263987227, 296.4150072818235, 296.4150072818235, 296.26064904211574, 296.30532672046087, 0.03537502595557663, 0.8835211260856368, 1.0154967670063433, 0.3233198693428126, 0.9206463117416827, 0.11787932777153287, 0.11787932777153277, 0.11787932777153269, 0.11787932777153291]) ## 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_2

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

state_mean = URL([ 6.519444444444445, 3.9837962962962963, 12.5, 16.78500002964779, 16.78491901104097, 16.785196788774595, 16.7854977140824, 72.89907407407408, 72.90567129629629, 72.909375, 72.91342592592592, 207.3190972222222, 207.3190972222222, 207.18541666666667, 207.23611111111111, 201.11863425925927, 201.11863425925927, 200.80648148148148, 200.88761574074073, 0.15636648599282596, 1.0591688615113237, 0.6963716355719771, 0.29117993655536184, 0.39915770157282743, 0.27310532142960087, 0.27310532142960087, 0.27310532142960087, 0.27310532142960087])

state_std = URL([ 3.4712575323780417, 2.001555126539623, 6.922187552431729, 3.553894201620696, 3.553811950901204, 3.5540391337133648, 3.5546125114989646, 16.542013984734275, 16.546533736927085, 16.54789739417334, 16.548964694872215, 291.88390011273555, 291.8839001127356, 291.7552778101503, 291.83391263987227, 296.4150072818235, 296.4150072818235, 296.26064904211574, 296.30532672046087, 0.03537502595557663, 0.8835211260856368, 1.0154967670063433, 0.3233198693428126, 0.9206463117416827, 0.11787932777153287, 0.11787932777153277, 0.11787932777153269, 0.11787932777153291]) ## 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_2

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

state_mean = URL([ 6.519444444444445, 3.9837962962962963, 12.5, 16.78500002964779, 16.78491901104097, 16.785196788774595, 16.7854977140824, 72.89907407407408, 72.90567129629629, 72.909375, 72.91342592592592, 207.3190972222222, 207.3190972222222, 207.18541666666667, 207.23611111111111, 201.11863425925927, 201.11863425925927, 200.80648148148148, 200.88761574074073, 0.15636648599282596, 1.0591688615113237, 0.6963716355719771, 0.29117993655536184, 0.39915770157282743, 0.27310532142960087, 0.27310532142960087, 0.27310532142960087, 0.27310532142960087])

state_std = URL([ 3.4712575323780417, 2.001555126539623, 6.922187552431729, 3.553894201620696, 3.553811950901204, 3.5540391337133648, 3.5546125114989646, 16.542013984734275, 16.546533736927085, 16.54789739417334, 16.548964694872215, 291.88390011273555, 291.8839001127356, 291.7552778101503, 291.83391263987227, 296.4150072818235, 296.4150072818235, 296.26064904211574, 296.30532672046087, 0.03537502595557663, 0.8835211260856368, 1.0154967670063433, 0.3233198693428126, 0.9206463117416827, 0.11787932777153287, 0.11787932777153277, 0.11787932777153269, 0.11787932777153291])## 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