Implements set_env_attr and get_env_attr for vector environments (#478)

close #473

test/base/test_env.py

@@ -166,10 +166,25 @@ def test_vecenv(size=10, num=8, sleep=0.001):
         for i, v in enumerate(venv):
             print(f'{type(v)}: {t[i]:.6f}s')
 
+    def assert_get(v, expected):
+        assert v.get_env_attr("size") == expected
+        assert v.get_env_attr("size", id=0) == [expected[0]]
+        assert v.get_env_attr("size", id=[0, 1, 2]) == expected[:3]
+
     for v in venv:
-        assert v.size == list(range(size, size + num))
+        assert_get(v, list(range(size, size + num)))
         assert v.env_num == num
         assert v.action_space == [Discrete(2)] * num
+
+        v.set_env_attr("size", 0)
+        assert_get(v, [0] * num)
+
+        v.set_env_attr("size", 1, 0)
+        assert_get(v, [1] + [0] * (num - 1))
+
+        v.set_env_attr("size", 2, [1, 2, 3])
+        assert_get(v, [1] + [2] * 3 + [0] * (num - 4))
+
     for v in venv:
         v.close()
 

test/throughput/test_batch_profile.py

@@ -10,7 +10,7 @@ from tianshou.data import Batch
 
 @pytest.fixture(scope="module")
 def data():
-    print("Initialising data...")
+    print("Initializing data...")
     np.random.seed(0)
     batch_set = [
         Batch(
@@ -19,7 +19,7 @@ def data():
                 'b1': (3.14, 3.14),
                 'b2': np.arange(1e3)
             },
-            c=i
+            c=i,
         ) for i in np.arange(int(1e4))
     ]
     batch0 = Batch(
@@ -27,8 +27,8 @@ def data():
         b=Batch(
             c=np.ones((1, ), dtype=np.float64),
             d=torch.ones((3, 3, 3), dtype=torch.float32),
-            e=list(range(3))
+            e=list(range(3)),
-        )
+        ),
     )
     batchs1 = [copy.deepcopy(batch0) for _ in np.arange(1e4)]
     batchs2 = [copy.deepcopy(batch0) for _ in np.arange(1e4)]
@@ -39,13 +39,13 @@ def data():
     indexs = np.random.choice(batch_len, size=batch_len // 10, replace=False)
     slice_dict = {
         'obs': [np.arange(20) for _ in np.arange(batch_len // 10)],
-        'reward': np.arange(batch_len // 10)
+        'reward': np.arange(batch_len // 10),
     }
     dict_set = [
         {
             'obs': np.arange(20),
             'info': "this is info",
-            'reward': 0
+            'reward': 0,
         } for _ in np.arange(1e2)
     ]
     batch4 = Batch(
@@ -53,11 +53,11 @@ def data():
         b=Batch(
             c=np.ones((1, ), dtype=np.float64),
             d=torch.ones((1000, 1000), dtype=torch.float32),
-            e=np.arange(1000)
+            e=np.arange(1000),
-        )
+        ),
     )
 
-    print("Initialised")
+    print("Initialized")
     return {
         'batch_set': batch_set,
         'batch0': batch0,
@@ -67,7 +67,7 @@ def data():
         'indexs': indexs,
         'dict_set': dict_set,
         'slice_dict': slice_dict,
-        'batch4': batch4
+        'batch4': batch4,
     }
 
 
@@ -106,7 +106,7 @@ def test_set_attr(data):
 
 def test_numpy_torch_convert(data):
     """Test conversion between numpy and torch."""
-    for _ in np.arange(1e5):
+    for _ in np.arange(1e4):  # not sure what's wrong in torch==1.10.0
         data['batch4'].to_torch()
         data['batch4'].to_numpy()
 

tianshou/env/venvs.py

@@ -130,17 +130,56 @@ class BaseVectorEnv(gym.Env):
         if key in [
             'metadata', 'reward_range', 'spec', 'action_space', 'observation_space'
         ]:  # reserved keys in gym.Env
-            return self.__getattr__(key)
+            return self.get_env_attr(key)
         else:
             return super().__getattribute__(key)
 
-    def __getattr__(self, key: str) -> List[Any]:
+    def get_env_attr(
-        """Fetch a list of env attributes.
+        self,
+        key: str,
+        id: Optional[Union[int, List[int], np.ndarray]] = None
+    ) -> List[Any]:
+        """Get an attribute from the underlying environments.
 
-        This function tries to retrieve an attribute from each individual wrapped
+        If id is an int, retrieve the attribute denoted by key from the environment
-        environment, if it does not belong to the wrapping vector environment class.
+        underlying the worker at index id. The result is returned as a list with one
+        element. Otherwise, retrieve the attribute for all workers at indices id and
+        return a list that is ordered correspondingly to id.
+
+        :param str key: The key of the desired attribute.
+        :param id: Indice(s) of the desired worker(s). Default to None for all env_id.
+
+        :return list: The list of environment attributes.
         """
-        return [getattr(worker, key) for worker in self.workers]
+        self._assert_is_not_closed()
+        id = self._wrap_id(id)
+        if self.is_async:
+            self._assert_id(id)
+
+        return [self.workers[j].get_env_attr(key) for j in id]
+
+    def set_env_attr(
+        self,
+        key: str,
+        value: Any,
+        id: Optional[Union[int, List[int], np.ndarray]] = None
+    ) -> None:
+        """Set an attribute in the underlying environments.
+
+        If id is an int, set the attribute denoted by key from the environment
+        underlying the worker at index id to value.
+        Otherwise, set the attribute for all workers at indices id.
+
+        :param str key: The key of the desired attribute.
+        :param Any value: The new value of the attribute.
+        :param id: Indice(s) of the desired worker(s). Default to None for all env_id.
+        """
+        self._assert_is_not_closed()
+        id = self._wrap_id(id)
+        if self.is_async:
+            self._assert_id(id)
+        for j in id:
+            self.workers[j].set_env_attr(key, value)
 
     def _wrap_id(
         self,

tianshou/env/worker/base.py

@@ -12,10 +12,14 @@ class EnvWorker(ABC):
         self._env_fn = env_fn
         self.is_closed = False
         self.result: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]
-        self.action_space = getattr(self, "action_space")  # noqa: B009
+        self.action_space = self.get_env_attr("action_space")  # noqa: B009
 
     @abstractmethod
-    def __getattr__(self, key: str) -> Any:
+    def get_env_attr(self, key: str) -> Any:
+        pass
+
+    @abstractmethod
+    def set_env_attr(self, key: str, value: Any) -> None:
         pass
 
     @abstractmethod

tianshou/env/worker/dummy.py

@@ -13,9 +13,12 @@ class DummyEnvWorker(EnvWorker):
         self.env = env_fn()
         super().__init__(env_fn)
 
-    def __getattr__(self, key: str) -> Any:
+    def get_env_attr(self, key: str) -> Any:
         return getattr(self.env, key)
 
+    def set_env_attr(self, key: str, value: Any) -> None:
+        setattr(self.env, key, value)
+
     def reset(self) -> Any:
         return self.env.reset()
 

tianshou/env/worker/ray.py

@@ -11,15 +11,27 @@ except ImportError:
     pass
 
 
+class _SetAttrWrapper(gym.Wrapper):
+
+    def set_env_attr(self, key: str, value: Any) -> None:
+        setattr(self.env, key, value)
+
+    def get_env_attr(self, key: str) -> Any:
+        return getattr(self.env, key)
+
+
 class RayEnvWorker(EnvWorker):
     """Ray worker used in RayVectorEnv."""
 
     def __init__(self, env_fn: Callable[[], gym.Env]) -> None:
-        self.env = ray.remote(gym.Wrapper).options(num_cpus=0).remote(env_fn())
+        self.env = ray.remote(_SetAttrWrapper).options(num_cpus=0).remote(env_fn())
         super().__init__(env_fn)
 
-    def __getattr__(self, key: str) -> Any:
+    def get_env_attr(self, key: str) -> Any:
-        return ray.get(self.env.__getattr__.remote(key))
+        return ray.get(self.env.get_env_attr.remote(key))
+
+    def set_env_attr(self, key: str, value: Any) -> None:
+        ray.get(self.env.set_env_attr.remote(key, value))
 
     def reset(self) -> Any:
         return ray.get(self.env.reset.remote())

tianshou/env/worker/subproc.py

@@ -107,6 +107,8 @@ def _worker(
                 p.send(env.seed(data) if hasattr(env, "seed") else None)
             elif cmd == "getattr":
                 p.send(getattr(env, data) if hasattr(env, data) else None)
+            elif cmd == "setattr":
+                setattr(env, data["key"], data["value"])
             else:
                 p.close()
                 raise NotImplementedError
@@ -140,10 +142,13 @@ class SubprocEnvWorker(EnvWorker):
         self.child_remote.close()
         super().__init__(env_fn)
 
-    def __getattr__(self, key: str) -> Any:
+    def get_env_attr(self, key: str) -> Any:
         self.parent_remote.send(["getattr", key])
         return self.parent_remote.recv()
 
+    def set_env_attr(self, key: str, value: Any) -> None:
+        self.parent_remote.send(["setattr", {"key": key, "value": value}])
+
     def _decode_obs(self) -> Union[dict, tuple, np.ndarray]:
 
         def decode_obs(