pytorch · vmoens · Sep 4, 2024 · Sep 3, 2024
diff --git a/.github/unittest/linux_libs/scripts_gym/environment.yml b/.github/unittest/linux_libs/scripts_gym/environment.yml
@@ -7,6 +7,7 @@ dependencies:
   - pip:
     # Initial version is required to install Atari ROMS in setup_env.sh
     - gym[atari]==0.13
+    - minigrid
     - hypothesis
     - future
     - cloudpickle

diff --git a/test/test_libs.py b/test/test_libs.py
@@ -153,6 +153,16 @@
 
 _has_meltingpot = importlib.util.find_spec("meltingpot") is not None
 
+_has_minigrid = importlib.util.find_spec("minigrid") is not None
+
+
+@pytest.fixture(scope="session", autouse=True)
+def maybe_init_minigrid():
+    if _has_minigrid and _has_gymnasium:
+        import minigrid
+
+        minigrid.register_minigrid_envs()
+
 
 def get_gym_pixel_wrapper():
     try:
@@ -1279,6 +1289,24 @@ def test_resetting_strategies(self, heterogeneous):
                 gc.collect()
 
 
+@pytest.mark.skipif(
+    not _has_minigrid or not _has_gymnasium, reason="MiniGrid not found"
+)
+class TestMiniGrid:
+    @pytest.mark.parametrize(
+        "id",
+        [
+            "BabyAI-KeyCorridorS6R3-v0",
+            "MiniGrid-Empty-16x16-v0",
+            "MiniGrid-BlockedUnlockPickup-v0",
+        ],
+    )
+    def test_minigrid(self, id):
+        env_base = gymnasium.make(id)
+        env = GymWrapper(env_base)
+        check_env_specs(env)
+
+
 @implement_for("gym", None, "0.26")
 def _make_gym_environment(env_name):  # noqa: F811
     gym = gym_backend()

diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py
@@ -12,10 +12,10 @@
 
 import numpy as np
 import torch
-from tensordict import TensorDict, TensorDictBase
+from tensordict import NonTensorData, TensorDict, TensorDictBase
 from torchrl._utils import logger as torchrl_logger
 
-from torchrl.data.tensor_specs import Composite, TensorSpec, Unbounded
+from torchrl.data.tensor_specs import Composite, NonTensor, TensorSpec, Unbounded
 from torchrl.envs.common import _EnvWrapper, EnvBase
 
 
@@ -283,9 +283,12 @@ def read_obs(
             observations = observations_dict
         else:
             for key, val in observations.items():
-                observations[key] = self.observation_spec[key].encode(
-                    val, ignore_device=True
-                )
+                if isinstance(self.observation_spec[key], NonTensor):
+                    observations[key] = NonTensorData(val)
+                else:
+                    observations[key] = self.observation_spec[key].encode(
+                        val, ignore_device=True
+                    )
         return observations
 
     def _step(self, tensordict: TensorDictBase) -> TensorDictBase:

diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py
@@ -29,6 +29,7 @@
     Composite,
     MultiCategorical,
     MultiOneHot,
+    NonTensor,
     OneHot,
     TensorSpec,
     Unbounded,
@@ -55,6 +56,14 @@
 
 _has_mo = importlib.util.find_spec("mo_gymnasium") is not None
 _has_sb3 = importlib.util.find_spec("stable_baselines3") is not None
+_has_minigrid = importlib.util.find_spec("minigrid") is not None
+
+
+def _minigrid_lib():
+    assert _has_minigrid, "minigrid not found"
+    import minigrid
+
+    return minigrid
 
 
 class set_gym_backend(_DecoratorContextManager):
@@ -369,6 +378,8 @@ def _gym_to_torchrl_spec_transform(
             categorical_action_encoding=categorical_action_encoding,
             remap_state_to_observation=remap_state_to_observation,
         )
+    elif _has_minigrid and isinstance(spec, _minigrid_lib().core.mission.MissionSpace):
+        return NonTensor((), device=device)
     else:
         raise NotImplementedError(
             f"spec of type {type(spec).__name__} is currently unaccounted for"
@@ -766,14 +777,20 @@ def __init__(self, env=None, categorical_action_encoding=False, **kwargs):
         self._seed_calls_reset = None
         self._categorical_action_encoding = categorical_action_encoding
         if env is not None:
-            if "EnvCompatibility" in str(
-                env
-            ):  # a hacky way of knowing if EnvCompatibility is part of the wrappers of env
-                raise ValueError(
-                    "GymWrapper does not support the gym.wrapper.compatibility.EnvCompatibility wrapper. "
-                    "If this feature is needed, detail your use case in an issue of "
-                    "https://github.com/pytorch/rl/issues."
-                )
+            try:
+                env_str = str(env)
+            except TypeError:
+                # MiniGrid has a bug where the __str__ method fails
+                pass
+            else:
+                if (
+                    "EnvCompatibility" in env_str
+                ):  # a hacky way of knowing if EnvCompatibility is part of the wrappers of env
+                    raise ValueError(
+                        "GymWrapper does not support the gym.wrapper.compatibility.EnvCompatibility wrapper. "
+                        "If this feature is needed, detail your use case in an issue of "
+                        "https://github.com/pytorch/rl/issues."
+                    )
             libname = self.get_library_name(env)
             with set_gym_backend(libname):
                 kwargs["env"] = env