BitFlippingEnv argument check and docs clarification (#1698)

* made change, not tested yet

* add back _obs_space with note on purpose

* match formatting

* update documentation

docs/misc/changelog.rst

@@ -26,6 +26,7 @@ Bug Fixes:
 - Fixed ``render_mode`` which was not properly loaded when using ``VecNormalize.load()``
 - Fixed success reward dtype in ``SimpleMultiObsEnv`` (@NixGD)
 - Fixed check_env for Sequence observation space (@corentinlger)
+- Prevents instantiating BitFlippingEnv with conflicting observation spaces (@kylesayrs)
 
 `SB3-Contrib`_
 ^^^^^^^^^^^^^^

stable_baselines3/common/envs/bit_flipping_env.py

@@ -13,15 +13,17 @@ class BitFlippingEnv(Env):
     Simple bit flipping env, useful to test HER.
     The goal is to flip all the bits to get a vector of ones.
     In the continuous variant, if the ith action component has a value > 0,
-    then the ith bit will be flipped.
+    then the ith bit will be flipped. Uses a ``MultiBinary`` observation space
+    by default.
 
     :param n_bits: Number of bits to flip
     :param continuous: Whether to use the continuous actions version or not,
         by default, it uses the discrete one
     :param max_steps: Max number of steps, by default, equal to n_bits
     :param discrete_obs_space: Whether to use the discrete observation
-        version or not, by default, it uses the ``MultiBinary`` one
-    :param image_obs_space: Use image as input instead of the ``MultiBinary`` one.
+        version or not, ie a one-hot encoding of all possible states
+    :param image_obs_space: Whether to use an image observation version
+        or not, ie a greyscale image of the state
     :param channel_first: Whether to use channel-first or last image.
     """
 
@@ -44,52 +46,11 @@ def __init__(
         self.image_shape = (1, 36, 36) if channel_first else (36, 36, 1)
         # The achieved goal is determined by the current state
         # here, it is a special where they are equal
-        if discrete_obs_space:
-            # In the discrete case, the agent act on the binary
-            # representation of the observation
-            self.observation_space = spaces.Dict(
-                {
-                    "observation": spaces.Discrete(2**n_bits),
-                    "achieved_goal": spaces.Discrete(2**n_bits),
-                    "desired_goal": spaces.Discrete(2**n_bits),
-                }
-            )
-        elif image_obs_space:
-            # When using image as input,
-            # one image contains the bits 0 -> 0, 1 -> 255
-            # and the rest is filled with zeros
-            self.observation_space = spaces.Dict(
-                {
-                    "observation": spaces.Box(
-                        low=0,
-                        high=255,
-                        shape=self.image_shape,
-                        dtype=np.uint8,
-                    ),
-                    "achieved_goal": spaces.Box(
-                        low=0,
-                        high=255,
-                        shape=self.image_shape,
-                        dtype=np.uint8,
-                    ),
-                    "desired_goal": spaces.Box(
-                        low=0,
-                        high=255,
-                        shape=self.image_shape,
-                        dtype=np.uint8,
-                    ),
-                }
-            )
-        else:
-            self.observation_space = spaces.Dict(
-                {
-                    "observation": spaces.MultiBinary(n_bits),
-                    "achieved_goal": spaces.MultiBinary(n_bits),
-                    "desired_goal": spaces.MultiBinary(n_bits),
-                }
-            )
 
-        self.obs_space = spaces.MultiBinary(n_bits)
+        # observation space for observations given to the model
+        self.observation_space = self._make_observation_space(discrete_obs_space, image_obs_space, n_bits)
+        # observation space used to update internal state
+        self._obs_space = spaces.MultiBinary(n_bits)
 
         if continuous:
             self.action_space = spaces.Box(-1, 1, shape=(n_bits,), dtype=np.float32)
@@ -105,7 +66,7 @@ def __init__(
         self.current_step = 0
 
     def seed(self, seed: int) -> None:
-        self.obs_space.seed(seed)
+        self._obs_space.seed(seed)
 
     def convert_if_needed(self, state: np.ndarray) -> Union[int, np.ndarray]:
         """
@@ -144,6 +105,64 @@ def convert_to_bit_vector(self, state: Union[int, np.ndarray], batch_size: int)
             bit_vector = np.array(state).reshape(batch_size, -1)
         return bit_vector
 
+    def _make_observation_space(self, discrete_obs_space: bool, image_obs_space: bool, n_bits: int) -> spaces.Dict:
+        """
+        Helper to create observation space
+
+        :param discrete_obs_space: Whether to use the discrete observation version
+        :param image_obs_space: Whether to use the image observation version
+        :param n_bits: The number of bits used to represent the state
+        :return: the environment observation space
+        """
+        if discrete_obs_space and image_obs_space:
+            raise ValueError("Cannot use both discrete and image observation spaces")
+
+        if discrete_obs_space:
+            # In the discrete case, the agent act on the binary
+            # representation of the observation
+            return spaces.Dict(
+                {
+                    "observation": spaces.Discrete(2**n_bits),
+                    "achieved_goal": spaces.Discrete(2**n_bits),
+                    "desired_goal": spaces.Discrete(2**n_bits),
+                }
+            )
+
+        if image_obs_space:
+            # When using image as input,
+            # one image contains the bits 0 -> 0, 1 -> 255
+            # and the rest is filled with zeros
+            return spaces.Dict(
+                {
+                    "observation": spaces.Box(
+                        low=0,
+                        high=255,
+                        shape=self.image_shape,
+                        dtype=np.uint8,
+                    ),
+                    "achieved_goal": spaces.Box(
+                        low=0,
+                        high=255,
+                        shape=self.image_shape,
+                        dtype=np.uint8,
+                    ),
+                    "desired_goal": spaces.Box(
+                        low=0,
+                        high=255,
+                        shape=self.image_shape,
+                        dtype=np.uint8,
+                    ),
+                }
+            )
+
+        return spaces.Dict(
+            {
+                "observation": spaces.MultiBinary(n_bits),
+                "achieved_goal": spaces.MultiBinary(n_bits),
+                "desired_goal": spaces.MultiBinary(n_bits),
+            }
+        )
+
     def _get_obs(self) -> Dict[str, Union[int, np.ndarray]]:
         """
         Helper to create the observation.
@@ -162,9 +181,9 @@ def reset(
         self, *, seed: Optional[int] = None, options: Optional[Dict] = None
     ) -> Tuple[Dict[str, Union[int, np.ndarray]], Dict]:
         if seed is not None:
-            self.obs_space.seed(seed)
+            self._obs_space.seed(seed)
         self.current_step = 0
-        self.state = self.obs_space.sample()
+        self.state = self._obs_space.sample()
         return self._get_obs(), {}
 
     def step(self, action: Union[np.ndarray, int]) -> GymStepReturn: