Update CityLearn.py

CityLearn.py

@@ -3,24 +3,17 @@ import datasets
 import numpy as np
 
 _DESCRIPTION = """The dataset consists of tuples of (observations, actions, rewards, dones) sampled by agents
-    interacting with the CityLearn 2022 Phase 1 environment"""
+    interacting with the CityLearn 2022 Phase 1 environment (only first 5 buildings)"""
 
 _BASE_URL = "https://huggingface.co/datasets/TobiTob/CityLearn/resolve/main"
 _URLS = {
-    "s_test": f"{_BASE_URL}/s_test.pkl",
-    "s_week": f"{_BASE_URL}/s_week.pkl",
-    "s_month": f"{_BASE_URL}/s_month.pkl",
-    "s_random": f"{_BASE_URL}/s_random.pkl",
-    "s_random2": f"{_BASE_URL}/s_random2.pkl",
-    "s_random3": f"{_BASE_URL}/s_random3.pkl",
-    "s_random4": f"{_BASE_URL}/s_random4.pkl",
     "random_230": f"{_BASE_URL}/random_230x5x38.pkl",
     "f_230": f"{_BASE_URL}/f_230x5x38.pkl",
     "f_50": f"{_BASE_URL}/f_50x5x1750.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",
-    "fnn_230": f"{_BASE_URL}/fnn_230x5x380.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",
@@ -37,61 +30,33 @@ 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="s_test",
-            description="Small dataset sampled from an expert policy in CityLearn environment. Data size 10x8",
-        ),
-        datasets.BuilderConfig(
-            name="s_week",
-            description="Data sampled from an expert policy in CityLearn environment. Data size 260x168",
-        ),
-        datasets.BuilderConfig(
-            name="s_month",
-            description="Data sampled from an expert policy in CityLearn environment. Data size 60x720",
-        ),
-        datasets.BuilderConfig(
-            name="s_random",
-            description="Random environment interactions in CityLearn environment. Data size 950x461",
-        ),
-        datasets.BuilderConfig(
-            name="s_random2",
-            description="Random environment interactions in CityLearn environment. Data size 43795x10",
-        ),
-        datasets.BuilderConfig(
-            name="s_random3",
-            description="Random environment interactions in CityLearn environment. Data size 23050x19",
-        ),
-        datasets.BuilderConfig(
-            name="s_random4",
-            description="Random environment interactions in CityLearn environment. Data size 437950x1",
-        ),
         datasets.BuilderConfig(
             name="random_230",
-            description="Random environment interactions in CityLearn environment. Sequence length = 230, Buildings = 5, Episodes = 1 ",
+            description="Random environment interactions. Sequence length = 230, Buildings = 5, Episodes = 1 ",
         ),
         datasets.BuilderConfig(
             name="f_230",
-            description="Data sampled from an expert policy in CityLearn environment. Sequence length = 230, Buildings = 5, Episodes = 1 ",
+            description="Data sampled from an expert LSTM policy. Sequence length = 230, Buildings = 5, Episodes = 1 ",
         ),
         datasets.BuilderConfig(
             name="f_50",
-            description="Data sampled from an expert policy in CityLearn environment. Sequence length = 50, Buildings = 5, Episodes = 10 ",
+            description="Data sampled from an expert LSTM policy with 10 episodes of repetition. Sequence length = 50, Buildings = 5, Episodes = 10 ",
         ),
         datasets.BuilderConfig(
             name="f_24",
-            description="Data sampled from an expert policy in CityLearn environment. Sequence length = 24, Buildings = 5, Episodes = 1 ",
+            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 policy in CityLearn environment. Used the new reward function. Sequence length = 24, Buildings = 5, Episodes = 1 ",
+            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 policy in CityLearn environment. Used the new reward function and changed some interactions with noise. Sequence length = 24, Buildings = 5, Episodes = 10 ",
+            description="Data sampled from an expert LSTM policy, extended with noise. Sequence length = 24, Buildings = 5, Episodes = 10 ",
         ),
         datasets.BuilderConfig(
-            name="fnn_230",
-            description="Data sampled from an expert policy in CityLearn environment. Used the new reward function and changed some interactions with noise. Sequence length = 230, Buildings = 5, Episodes = 10 ",
+            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",