TobiTob
/

decision_transformer_2

decision_transformer

generated_from_trainer

Inference Endpoints

Model card Files Files and versions Metrics Training metrics Community

TobiTob commited on Feb 24, 2023

Commit

8b989eb

•

1 Parent(s): fde10bc

Update README.md

Files changed (1) hide show

README.md +1 -0

README.md CHANGED Viewed

@@ -18,6 +18,7 @@ This model is a fine-tuned version of [](https://huggingface.co/) on the city_le
 ## 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])

 ## 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])