[Versioning] Gymnasium 1.0 incompatibility errors

test/_utils_internal.py

@@ -121,7 +121,7 @@ def _set_gym_environments():  # noqa: F811
     _BREAKOUT_VERSIONED = "ALE/Breakout-v5"
 
 
-@implement_for("gymnasium")
+@implement_for("gymnasium", None, "1.0.0")
 def _set_gym_environments():  # noqa: F811
     global _CARTPOLE_VERSIONED, _HALFCHEETAH_VERSIONED, _PENDULUM_VERSIONED, _PONG_VERSIONED, _BREAKOUT_VERSIONED
 

test/test_libs.py

@@ -277,7 +277,7 @@ def _make_spec(  # noqa: F811
             shape=batch_size,
         )
 
-    @implement_for("gymnasium")
+    @implement_for("gymnasium", None, "1.0.0")
     def _make_spec(  # noqa: F811
         self, batch_size, cat, cat_shape, multicat, multicat_shape
     ):
@@ -322,7 +322,7 @@ def test_gym_spec_cast_tuple_sequential(self, order):
 
     # @pytest.mark.parametrize("order", ["seq_tuple", "tuple_seq"])
     @pytest.mark.parametrize("order", ["tuple_seq"])
-    @implement_for("gymnasium")
+    @implement_for("gymnasium", None, "1.0.0")
     def test_gym_spec_cast_tuple_sequential(self, order):  # noqa: F811
         with set_gym_backend("gymnasium"):
             if order == "seq_tuple":
@@ -838,7 +838,7 @@ def info_reader(info, tensordict):
         finally:
             set_gym_backend(gb).set()
 
-    @implement_for("gymnasium")
+    @implement_for("gymnasium", None, "1.0.0")
     def test_one_hot_and_categorical(self):
         # tests that one-hot and categorical work ok when an integer is expected as action
         cliff_walking = GymEnv("CliffWalking-v0", categorical_action_encoding=True)
@@ -857,7 +857,7 @@ def test_one_hot_and_categorical(self):  # noqa: F811
         # versions.
         return
 
-    @implement_for("gymnasium")
+    @implement_for("gymnasium", None, "1.0.0")
     @pytest.mark.parametrize(
         "envname",
         ["HalfCheetah-v4", "CartPole-v1", "ALE/Pong-v5"]
@@ -883,7 +883,7 @@ def test_vecenvs_wrapper(self, envname):
         assert env.batch_size == torch.Size([2])
         check_env_specs(env)
 
-    @implement_for("gymnasium")
+    @implement_for("gymnasium", None, "1.0.0")
     # this env has Dict-based observation which is a nice thing to test
     @pytest.mark.parametrize(
         "envname",
@@ -1045,7 +1045,7 @@ def test_gym_output_num(self, wrapper):  # noqa: F811
         finally:
             set_gym_backend(gym).set()
 
-    @implement_for("gymnasium")
+    @implement_for("gymnasium", None, "1.0.0")
     @pytest.mark.parametrize("wrapper", [True, False])
     def test_gym_output_num(self, wrapper):  # noqa: F811
         # gym has 5 outputs, with truncation
@@ -1148,7 +1148,7 @@ def test_vecenvs_nan(self):  # noqa: F811
         del c
         return
 
-    @implement_for("gymnasium")
+    @implement_for("gymnasium", None, "1.0.0")
     def test_vecenvs_nan(self):  # noqa: F811
         # new versions of gym must never return nan for next values when there is a done state
         torch.manual_seed(0)
@@ -1319,7 +1319,7 @@ def _make_gym_environment(env_name):  # noqa: F811
     return gym.make(env_name, render_mode="rgb_array")
 
 
-@implement_for("gymnasium")
+@implement_for("gymnasium", None, "1.0.0")
 def _make_gym_environment(env_name):  # noqa: F811
     gym = gym_backend()
     return gym.make(env_name, render_mode="rgb_array")

torchrl/_utils.py

@@ -269,7 +269,7 @@ class implement_for:
         ...     # More recent gym versions will return x + 2
         ...     return x + 2
         ...
-        >>> @implement_for("gymnasium")
+        >>> @implement_for("gymnasium", None, "1.0.0")
         >>> def fun(self, x):
         ...     # If gymnasium is to be used instead of gym, x+3 will be returned
         ...     return x + 3

torchrl/envs/libs/_gym_utils.py

@@ -14,7 +14,7 @@
 from torchrl._utils import implement_for
 from torchrl.data import Composite
 from torchrl.envs import step_mdp, TransformedEnv
-from torchrl.envs.libs.gym import _torchrl_to_gym_spec_transform
+from torchrl.envs.libs.gym import _torchrl_to_gym_spec_transform, GYMNASIUM_1_ERROR
 
 _has_gym = importlib.util.find_spec("gym", None) is not None
 _has_gymnasium = importlib.util.find_spec("gymnasium", None) is not None
@@ -125,7 +125,11 @@ def _action_keys(self):
     import gymnasium
 
     class _TorchRLGymnasiumWrapper(gymnasium.Env, _BaseGymWrapper):
-        @implement_for("gymnasium")
+        @implement_for("gymnasium", "1.0.0")
+        def step(self, action):  # noqa: F811
+            raise ImportError(GYMNASIUM_1_ERROR)
+
+        @implement_for("gymnasium", None, "1.0.0")
         def step(self, action):  # noqa: F811
             action_keys = self._action_keys
             if len(action_keys) == 1:
@@ -153,7 +157,7 @@ def step(self, action):  # noqa: F811
                 out = tree_map(lambda x: x.detach().cpu().numpy(), out)
             return out
 
-        @implement_for("gymnasium")
+        @implement_for("gymnasium", None, "1.0.0")
         def reset(self):  # noqa: F811
             self._tensordict = self.torchrl_env.reset()
             observation = self._tensordict
@@ -167,6 +171,10 @@ def reset(self):  # noqa: F811
                 out = tree_map(lambda x: x.detach().cpu().numpy(), out)
             return out
 
+        @implement_for("gymnasium", "1.0.0")
+        def reset(self):  # noqa: F811
+            raise ImportError(GYMNASIUM_1_ERROR)
+
 else:
 
     class _TorchRLGymnasiumWrapper:

torchrl/envs/libs/gym.py

@@ -59,6 +59,22 @@
 _has_minigrid = importlib.util.find_spec("minigrid") is not None
 
 
+GYMNASIUM_1_ERROR = """RuntimeError: TorchRL does not support gymnasium 1.0 or later versions due to incompatible
+changes in the Gym API.
+Using gymnasium 1.0 with TorchRL would require significant modifications to your code and may result in:
+* Inaccurate step counting, as the auto-reset feature can cause unpredictable numbers of steps to be executed.
+* Potential data corruption, as the environment may require/produce garbage data during reset steps.
+* Trajectory overlap during data collection.
+* Increased computational overhead, as the library would need to handle the additional complexity of auto-resets.
+* Manual filtering and boilerplate code to mitigate these issues, which would compromise the modularity and ease of
+use of TorchRL.
+To maintain the integrity and efficiency of our library, we cannot support this version of gymnasium at this time.
+If you need to use gymnasium 1.0 or later, we recommend exploring alternative solutions or waiting for future updates
+to TorchRL and gymnasium that may address this compatibility issue.
+For more information, please refer to discussion https://github.com/pytorch/rl/discussions/2483 in torchrl.
+"""
+
+
 def _minigrid_lib():
     assert _has_minigrid, "minigrid not found"
     import minigrid
@@ -400,27 +416,37 @@ def _box_convert(spec, gym_spaces, shape):  # noqa: F811
     return gym_spaces.Box(low=low, high=high, shape=shape)
 
 
-@implement_for("gymnasium")
+@implement_for("gymnasium", None, "1.0.0")
 def _box_convert(spec, gym_spaces, shape):  # noqa: F811
     low = spec.low.detach().cpu().numpy()
     high = spec.high.detach().cpu().numpy()
     return gym_spaces.Box(low=low, high=high, shape=shape)
 
 
+@implement_for("gymnasium", "1.0.0")
+def _box_convert(spec, gym_spaces, shape):  # noqa: F811
+    raise ImportError(GYMNASIUM_1_ERROR)
+
+
 @implement_for("gym", "0.21", None)
 def _multidiscrete_convert(gym_spaces, spec):
     return gym_spaces.multi_discrete.MultiDiscrete(
         spec.nvec, dtype=torch_to_numpy_dtype_dict[spec.dtype]
     )
 
 
-@implement_for("gymnasium")
+@implement_for("gymnasium", None, "1.0.0")
 def _multidiscrete_convert(gym_spaces, spec):  # noqa: F811
     return gym_spaces.multi_discrete.MultiDiscrete(
         spec.nvec, dtype=torch_to_numpy_dtype_dict[spec.dtype]
     )
 
 
+@implement_for("gymnasium", "1.0.0")
+def _multidiscrete_convert(gym_spaces, spec):  # noqa: F811
+    raise ImportError(GYMNASIUM_1_ERROR)
+
+
 @implement_for("gym", None, "0.21")
 def _multidiscrete_convert(gym_spaces, spec):  # noqa: F811
     return gym_spaces.multi_discrete.MultiDiscrete(spec.nvec)
@@ -519,12 +545,17 @@ def _get_gym_envs():  # noqa: F811
     return gym.envs.registration.registry.keys()
 
 
-@implement_for("gymnasium")
+@implement_for("gymnasium", None, "1.0.0")
 def _get_gym_envs():  # noqa: F811
     gym = gym_backend()
     return gym.envs.registration.registry.keys()
 
 
+@implement_for("gymnasium", "1.0.0")
+def _get_gym_envs():  # noqa: F811
+    raise ImportError(GYMNASIUM_1_ERROR)
+
+
 def _is_from_pixels(env):
     observation_spec = env.observation_space
     try:
@@ -835,7 +866,7 @@ def _get_batch_size(self, env):
             batch_size = self.batch_size
         return batch_size
 
-    @implement_for("gymnasium")  # gymnasium wants the unwrapped env
+    @implement_for("gymnasium", None, "1.0.0")  # gymnasium wants the unwrapped env
     def _get_batch_size(self, env):  # noqa: F811
         env_unwrapped = env.unwrapped
         if hasattr(env_unwrapped, "num_envs"):
@@ -844,6 +875,10 @@ def _get_batch_size(self, env):  # noqa: F811
             batch_size = self.batch_size
         return batch_size
 
+    @implement_for("gymnasium", "1.0.0")
+    def _get_batch_size(self, env):  # noqa: F811
+        raise ImportError(GYMNASIUM_1_ERROR)
+
     def _check_kwargs(self, kwargs: Dict):
         if "env" not in kwargs:
             raise TypeError("Could not find environment key 'env' in kwargs.")
@@ -920,7 +955,11 @@ def _build_gym_env(self, env, pixels_only):  # noqa: F811
 
         return LegacyPixelObservationWrapper(env, pixels_only=pixels_only)
 
-    @implement_for("gymnasium")
+    @implement_for("gymnasium", "1.0.0")
+    def _build_gym_env(self, env, pixels_only):  # noqa: F811
+        raise ImportError(GYMNASIUM_1_ERROR)
+
+    @implement_for("gymnasium", None, "1.0.0")
     def _build_gym_env(self, env, pixels_only):  # noqa: F811
         compatibility = gym_backend("wrappers.compatibility")
         pixel_observation = gym_backend("wrappers.pixel_observation")
@@ -985,7 +1024,11 @@ def _set_seed_initial(self, seed: int) -> None:  # noqa: F811
             except AttributeError as err2:
                 raise err from err2
 
-    @implement_for("gymnasium")
+    @implement_for("gymnasium", "1.0.0")
+    def _set_seed_initial(self, seed: int) -> None:  # noqa: F811
+        raise ImportError(GYMNASIUM_1_ERROR)
+
+    @implement_for("gymnasium", None, "1.0.0")
     def _set_seed_initial(self, seed: int) -> None:  # noqa: F811
         try:
             self.reset(seed=seed)
@@ -1003,7 +1046,11 @@ def _reward_space(self, env):
         if hasattr(env, "reward_space") and env.reward_space is not None:
             return env.reward_space
 
-    @implement_for("gymnasium")
+    @implement_for("gymnasium", "1.0.0")
+    def _reward_space(self, env):  # noqa: F811
+        raise ImportError(GYMNASIUM_1_ERROR)
+
+    @implement_for("gymnasium", None, "1.0.0")
     def _reward_space(self, env):  # noqa: F811
         env = env.unwrapped
         if hasattr(env, "reward_space") and env.reward_space is not None:
@@ -1397,7 +1444,14 @@ def _set_gym_args(  # noqa: F811
     ) -> None:
         kwargs.setdefault("disable_env_checker", True)
 
-    @implement_for("gymnasium")
+    @implement_for("gymnasium", "1.0.0")
+    def _set_gym_args(  # noqa: F811
+        self,
+        kwargs,
+    ) -> None:
+        raise ImportError(GYMNASIUM_1_ERROR)
+
+    @implement_for("gymnasium", None, "1.0.0")
     def _set_gym_args(  # noqa: F811
         self,
         kwargs,