Update CityLearn.py

CityLearn.py

@@ -7,20 +7,8 @@ _DESCRIPTION = """The dataset consists of tuples of (observations, actions, rewa
 
 _BASE_URL = "https://huggingface.co/datasets/TobiTob/CityLearn/resolve/main"
 _URLS = {
-    "random_230": f"{_BASE_URL}/random_230x5x38.pkl",
     "f_230": f"{_BASE_URL}/f_230x5x38.pkl",
-    "f_24": f"{_BASE_URL}/f_24x5x364.pkl",
-    "fr_24": f"{_BASE_URL}/fr_24x5x364.pkl",
-    "fn_24": f"{_BASE_URL}/fn_24x5x3649.pkl",
-    "fn_230": f"{_BASE_URL}/fnn_230x5x380.pkl",
-    "rb_24": f"{_BASE_URL}/rb_24x5x364.pkl",
-    "rb_50": f"{_BASE_URL}/rb_50x5x175.pkl",
-    "rb_108": f"{_BASE_URL}/rb_108x5x81.pkl",
     "rb_230": f"{_BASE_URL}/rb_230x5x38.pkl",
-    "rb_461": f"{_BASE_URL}/rb_461x5x19.pkl",
-    "rb_973": f"{_BASE_URL}/rb_973x5x9.pkl",
-    "rb_2189": f"{_BASE_URL}/rb_2189x5x4.pkl",
-    "rbn_24": f"{_BASE_URL}/rb_24x5x18247.pkl",
 }
 
 
@@ -29,62 +17,14 @@ class DecisionTransformerCityLearnDataset(datasets.GeneratorBasedBuilder):
     # You will be able to load one configuration in the following list with
     # data = datasets.load_dataset('TobiTob/CityLearn', 'data_name')
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="random_230",
-            description="Random environment interactions. Sequence length = 230, Buildings = 5, Episodes = 1 ",
-        ),
         datasets.BuilderConfig(
             name="f_230",
             description="Data sampled from an expert LSTM policy. Sequence length = 230, Buildings = 5, Episodes = 1 ",
         ),
-        datasets.BuilderConfig(
-            name="f_24",
-            description="Data sampled from an expert LSTM policy. Used the old reward function. Sequence length = 24, Buildings = 5, Episodes = 1 ",
-        ),
-        datasets.BuilderConfig(
-            name="fr_24",
-            description="Data sampled from an expert LSTM policy. Used the new reward function. Sequence length = 24, Buildings = 5, Episodes = 1 ",
-        ),
-        datasets.BuilderConfig(
-            name="fn_24",
-            description="Data sampled from an expert LSTM policy, extended with noise. Sequence length = 24, Buildings = 5, Episodes = 10 ",
-        ),
-        datasets.BuilderConfig(
-            name="fn_230",
-            description="Data sampled from an expert LSTM policy, extended with noise. Sequence length = 230, Buildings = 5, Episodes = 10 ",
-        ),
-        datasets.BuilderConfig(
-            name="rb_24",
-            description="Data sampled from a simple rule based policy. Used the new reward function. Sequence length = 24, Buildings = 5, Episodes = 1 ",
-        ),
-        datasets.BuilderConfig(
-            name="rb_50",
-            description="Data sampled from a simple rule based policy. Used the new reward function. Sequence length = 50, Buildings = 5, Episodes = 1 ",
-        ),
-        datasets.BuilderConfig(
-            name="rb_108",
-            description="Data sampled from a simple rule based policy. Used the new reward function. Sequence length = 108, Buildings = 5, Episodes = 1 ",
-        ),
         datasets.BuilderConfig(
             name="rb_230",
             description="Data sampled from a simple rule based policy. Used the new reward function. Sequence length = 230, Buildings = 5, Episodes = 1 ",
         ),
-        datasets.BuilderConfig(
-            name="rb_461",
-            description="Data sampled from a simple rule based policy. Used the new reward function. Sequence length = 461, Buildings = 5, Episodes = 1 ",
-        ),
-        datasets.BuilderConfig(
-            name="rb_973",
-            description="Data sampled from a simple rule based policy. Used the new reward function. Sequence length = 973, Buildings = 5, Episodes = 1 ",
-        ),
-        datasets.BuilderConfig(
-            name="rb_2189",
-            description="Data sampled from a simple rule based policy. Used the new reward function. Sequence length = 2189, Buildings = 5, Episodes = 1 ",
-        ),
-        datasets.BuilderConfig(
-            name="rbn_24",
-            description="Data sampled from a simple rule based policy. Used the new reward function and changed some interactions with noise. Sequence length = 24, Buildings = 5, Episodes = 50 ",
-        ),
     ]
 
     def _info(self):