fixed type check issues

reproductions/offline/qdt.py

@@ -31,7 +31,14 @@ def main() -> None:
 
     # first fit Q-learning algorithm to the dataset
     if args.model_file is not None:
-        q_algo = d3rlpy.load_learnable(args.model_file)
+        # load model and assert type
+        q_algo_loaded = d3rlpy.load_learnable(args.model_file)
+        if not isinstance(q_algo_loaded, (CQL, IQL)):
+            raise ValueError(
+                "The loaded model is not an instance of CQL or IQL."
+            )
+        # cast to the expected type
+        q_algo = q_algo_loaded
     else:
         if args.q_learning_type == "cql":
             q_algo = fit_cql(dataset, env, args.seed, args.gpu, timestamp)
@@ -40,6 +47,9 @@ def main() -> None:
 
     # relabel dataset RTGs with the learned value functions
     print("Relabeling dataset with RTGs...")
+    if not isinstance(dataset._buffer, InfiniteBuffer):
+        raise ValueError("Dataset must be an InfiniteBuffer.")
+
     relabel_dataset_rtg(
         dataset._buffer, q_algo, args.context_size, seed=args.seed
     )
@@ -49,6 +59,8 @@ def main() -> None:
         dataset, env, args.context_size, args.seed, args.gpu, False, timestamp
     )
 
+    return
+
 
 """ --------------------------------------------------------------------
     Aargument dataset
@@ -57,11 +69,11 @@ def main() -> None:
 
 def relabel_dataset_rtg(
     buffer: InfiniteBuffer,
-    q_algo: Union["CQL", "IQL"],
+    q_algo: Union[CQL, IQL],
     k: int,
     num_action_samples: int = 10,
     seed: int = 0,
-):
+) -> None:
     """
     Relabel RTG (reward-to-go) to the given dataset using the given Q-function.
 
@@ -109,6 +121,8 @@ def relabel_dataset_rtg(
 
         prev_idx = idx
 
+    return
+
 
 """ --------------------------------------------------------------------
     Fit offline RL algorithms to the given dataset.