Fix master branch test issues (#908)

setup.py

@@ -30,7 +30,7 @@ def get_install_requires() -> str:
 def get_extras_require() -> str:
     req = {
         "dev": [
-            "sphinx",
+            "sphinx<7",
             "sphinx_rtd_theme",
             "jinja2",
             "sphinxcontrib-bibtex",

test/base/test_batch.py

@@ -38,6 +38,7 @@ def test_batch():
     assert 'a' not in b
     with pytest.raises(AssertionError):
         Batch({1: 2})
+    batch = Batch(a=[torch.ones(3), torch.ones(3)])
     assert Batch(a=[np.zeros((2, 3)), np.zeros((3, 3))]).a.dtype == object
     with pytest.raises(TypeError):
         Batch(a=[np.zeros((3, 2)), np.zeros((3, 3))])
@@ -47,7 +48,6 @@ def test_batch():
         Batch(a=[torch.zeros((3, 3)), np.zeros((3, 3))])
     with pytest.raises(TypeError):
         Batch(a=[1, np.zeros((3, 3)), torch.zeros((3, 3))])
-    batch = Batch(a=[torch.ones(3), torch.ones(3)])
     assert torch.allclose(batch.a, torch.ones(2, 3))
     batch.cat_(batch)
     assert torch.allclose(batch.a, torch.ones(4, 3))
@@ -183,7 +183,7 @@ def test_batch():
     g1.add_nodes_from(list(range(10)))
     g2 = nx.Graph()
     g2.add_nodes_from(list(range(20)))
-    assert Batch(a=np.array([g1, g2])).a.dtype == object
+    assert Batch(a=np.array([g1, g2], dtype=object)).a.dtype == object
 
 
 def test_batch_over_batch():

test/base/test_buffer.py

@@ -32,7 +32,7 @@ def test_replaybuffer(size=10, bufsize=20):
     buf = ReplayBuffer(bufsize)
     buf.update(buf)
     assert str(buf) == buf.__class__.__name__ + '()'
-    obs = env.reset()
+    obs, _ = env.reset()
     action_list = [1] * 5 + [0] * 10 + [1] * 10
     for i, act in enumerate(action_list):
         obs_next, rew, terminated, truncated, info = env.step(act)

tianshou/data/batch.py

@@ -58,7 +58,10 @@ def _to_array_with_correct_type(obj: Any) -> np.ndarray:
     # convert the value to np.ndarray
     # convert to object obj type if neither bool nor number
     # raises an exception if array's elements are tensors themselves
-    obj_array = np.asanyarray(obj)
+    try:
+        obj_array = np.asanyarray(obj)
+    except ValueError:
+        obj_array = np.asanyarray(obj, dtype=object)
     if not issubclass(obj_array.dtype.type, (np.bool_, np.number)):
         obj_array = obj_array.astype(object)
     if obj_array.dtype == object:

tianshou/data/buffer/cached.py

@@ -34,7 +34,7 @@ class CachedReplayBuffer(ReplayBufferManager):
         max_episode_length: int,
     ) -> None:
         assert cached_buffer_num > 0 and max_episode_length > 0
-        assert type(main_buffer) == ReplayBuffer
+        assert isinstance(main_buffer, ReplayBuffer)
         kwargs = main_buffer.options
         buffers = [main_buffer] + [
             ReplayBuffer(max_episode_length, **kwargs)

tianshou/data/collector.py

@@ -9,6 +9,7 @@ import torch
 from tianshou.data import (
     Batch,
     CachedReplayBuffer,
+    PrioritizedReplayBuffer,
     ReplayBuffer,
     ReplayBufferManager,
     VectorReplayBuffer,
@@ -89,10 +90,10 @@ class Collector(object):
         else:  # ReplayBuffer or PrioritizedReplayBuffer
             assert buffer.maxsize > 0
             if self.env_num > 1:
-                if type(buffer) == ReplayBuffer:
+                if isinstance(buffer, ReplayBuffer):
                     buffer_type = "ReplayBuffer"
                     vector_type = "VectorReplayBuffer"
-                else:
+                if isinstance(buffer, PrioritizedReplayBuffer):
                     buffer_type = "PrioritizedReplayBuffer"
                     vector_type = "PrioritizedVectorReplayBuffer"
                 raise TypeError(