diff --git a/test/base/test_batch.py b/test/base/test_batch.py
index be88a5a..b7adeb0 100644
--- a/test/base/test_batch.py
+++ b/test/base/test_batch.py
@@ -54,6 +54,9 @@ def test_batch_over_batch():
     assert batch3.c == [6, 7, 8, 6, 7, 8]
     assert batch3.b.a == [3, 4, 5, 3, 4, 5]
     assert batch3.b.b == [4, 5, 6, 4, 5, 6]
+    batch4 = Batch(({'a': {'b': np.array([1.0])}},))
+    assert batch4.a.b.ndim == 2
+    assert batch4.a.b[0, 0] == 1.0
 
 
 def test_batch_cat_and_stack():
diff --git a/tianshou/data/batch.py b/tianshou/data/batch.py
index 9572db4..e0fe605 100644
--- a/tianshou/data/batch.py
+++ b/tianshou/data/batch.py
@@ -3,7 +3,7 @@ import copy
 import pprint
 import warnings
 import numpy as np
-from typing import Any, List, Union, Iterator, Optional
+from typing import Any, List, Tuple, Union, Iterator, Optional
 
 # Disable pickle warning related to torch, since it has been removed
 # on torch master branch. See Pull Request #39003 for details:
@@ -76,29 +76,28 @@ class Batch:
 
     def __init__(self,
                  batch_dict: Optional[
-                     Union[dict, List[dict], np.ndarray]] = None,
+                     Union[dict, Tuple[dict], List[dict], np.ndarray]] = None,
                  **kwargs) -> None:
-        if isinstance(batch_dict, (list, np.ndarray)) \
+        if isinstance(batch_dict, (list, tuple, np.ndarray)) \
                 and len(batch_dict) > 0 and isinstance(batch_dict[0], dict):
             for k, v in zip(batch_dict[0].keys(),
                             zip(*[e.values() for e in batch_dict])):
-                if isinstance(v, (list, np.ndarray)) \
-                        and len(v) > 0 and isinstance(v[0], dict):
-                    self.__dict__[k] = Batch.stack([Batch(v_) for v_ in v])
+                if isinstance(v[0], dict) \
+                        or (isinstance(v, (list, tuple, np.ndarray))
+                            and len(v) > 0 and isinstance(v[0], dict)):
+                    self.__dict__[k] = Batch(v)
                 elif isinstance(v[0], np.ndarray):
                     self.__dict__[k] = np.stack(v, axis=0)
                 elif isinstance(v[0], torch.Tensor):
                     self.__dict__[k] = torch.stack(v, dim=0)
                 elif isinstance(v[0], Batch):
                     self.__dict__[k] = Batch.stack(v)
-                elif isinstance(v[0], dict):
-                    self.__dict__[k] = Batch(v)
                 else:
                     self.__dict__[k] = list(v)
         elif isinstance(batch_dict, dict):
             for k, v in batch_dict.items():
                 if isinstance(v, dict) \
-                        or (isinstance(v, (list, np.ndarray))
+                        or (isinstance(v, (list, tuple, np.ndarray))
                             and len(v) > 0 and isinstance(v[0], dict)):
                     self.__dict__[k] = Batch(v)
                 else: