Update CityLearn.py

CityLearn.py

@@ -17,6 +17,10 @@ _URLS = {
     "f_50": f"{_BASE_URL}/f_50x5x1750.pkl",
     "fr_24": f"{_BASE_URL}/fr_24x5x364.pkl",
     "fn_24": f"{_BASE_URL}/fn_24x5x3649.pkl",
+    "rb_24": f"{_BASE_URL}/rb_24x5x364.pkl",
+    "rb_230": f"{_BASE_URL}/rb_230x5x38.pkl",
+    "rb_2189": f"{_BASE_URL}/rb_2189x5x4.pkl",
+    "rbn_24": f"{_BASE_URL}/rb_24x5x18247.pkl",
 }
 
 
@@ -65,6 +69,22 @@ class DecisionTransformerCityLearnDataset(datasets.GeneratorBasedBuilder):
             name="fn_24",
             description="Data sampled from an expert policy in CityLearn environment. Used the new reward function and changed some interactions with noise. Sequence length = 24, 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_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_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):