amend

docs/source/reference/envs.rst

@@ -768,6 +768,7 @@ to be able to create this other composition:
 
     Transform
     TransformedEnv
+    ActionDiscretizer
     ActionMask
     AutoResetEnv
     AutoResetTransform
@@ -779,17 +780,16 @@ to be able to create this other composition:
     CenterCrop
     ClipTransform
     Compose
+    DTypeCastTransform
     DeviceCastTransform
     DiscreteActionProjection
     DoubleToFloat
-    DTypeCastTransform
     EndOfLifeTransform
     ExcludeTransform
     FiniteTensorDictCheck
     FlattenObservation
     FrameSkipTransform
     GrayScale
-    gSDENoise
     InitTracker
     KLRewardTransform
     NoopResetEnv
@@ -799,13 +799,13 @@ to be able to create this other composition:
     PinMemoryTransform
     R3MTransform
     RandomCropTensorDict
+    RemoveEmptySpecs
     RenameTransform
     Resize
+    Reward2GoTransform
     RewardClipping
     RewardScaling
     RewardSum
-    Reward2GoTransform
-    RemoveEmptySpecs
     SelectTransform
     SignTransform
     SqueezeTransform
@@ -815,11 +815,12 @@ to be able to create this other composition:
     TimeMaxPool
     ToTensorImage
     UnsqueezeTransform
-    VecGymEnvTransform
-    VecNorm
     VC1Transform
     VIPRewardTransform
     VIPTransform
+    VecGymEnvTransform
+    VecNorm
+    gSDENoise
 
 Environments with masked actions
 --------------------------------

test/test_transforms.py

@@ -119,6 +119,7 @@
 from torchrl.envs.transforms.rlhf import KLRewardTransform
 from torchrl.envs.transforms.transforms import (
     _has_tv,
+    ActionDiscretizer,
     BatchSizeTransform,
     FORWARD_NOT_IMPLEMENTED,
     Transform,
@@ -10985,6 +10986,123 @@ def test_transform_inverse(self):
         return
 
 
+class TestActionDiscretizer(TransformBase):
+    @pytest.mark.parametrize("categorical", [True, False])
+    def test_single_trans_env_check(self, categorical):
+        base_env = ContinuousActionVecMockEnv()
+        env = base_env.append_transform(
+            ActionDiscretizer(num_intervals=5, categorical=categorical)
+        )
+        check_env_specs(env)
+
+    @pytest.mark.parametrize("categorical", [True, False])
+    def test_serial_trans_env_check(self, categorical):
+        def make_env():
+            base_env = ContinuousActionVecMockEnv()
+            return base_env.append_transform(
+                ActionDiscretizer(num_intervals=5, categorical=categorical)
+            )
+
+        env = SerialEnv(2, make_env)
+        check_env_specs(env)
+
+    @pytest.mark.parametrize("categorical", [True, False])
+    def test_parallel_trans_env_check(self, categorical):
+        def make_env():
+            base_env = ContinuousActionVecMockEnv()
+            env = base_env.append_transform(
+                ActionDiscretizer(num_intervals=5, categorical=categorical)
+            )
+            return env
+
+        env = ParallelEnv(2, make_env, mp_start_method="fork")
+        check_env_specs(env)
+
+    @pytest.mark.parametrize("categorical", [True, False])
+    def test_trans_serial_env_check(self, categorical):
+        env = SerialEnv(2, ContinuousActionVecMockEnv).append_transform(
+            ActionDiscretizer(num_intervals=5, categorical=categorical)
+        )
+        check_env_specs(env)
+
+    @pytest.mark.parametrize("categorical", [True, False])
+    def test_trans_parallel_env_check(self, categorical):
+        env = ParallelEnv(
+            2, ContinuousActionVecMockEnv, mp_start_method="fork"
+        ).append_transform(ActionDiscretizer(num_intervals=5, categorical=categorical))
+        check_env_specs(env)
+
+    def test_transform_no_env(self):
+        categorical = True
+        with pytest.raises(RuntimeError, match="Cannot execute transform"):
+            ActionDiscretizer(num_intervals=5, categorical=categorical)._init()
+
+    def test_transform_compose(self):
+        categorical = True
+        env = SerialEnv(2, ContinuousActionVecMockEnv).append_transform(
+            Compose(ActionDiscretizer(num_intervals=5, categorical=categorical))
+        )
+        check_env_specs(env)
+
+    @pytest.mark.skipif(not _has_gym, reason="gym required for this test")
+    @pytest.mark.parametrize("envname", ["cheetah", "pendulum"])
+    @pytest.mark.parametrize("interval_as_tensor", [False, True])
+    @pytest.mark.parametrize("categorical", [True, False])
+    @pytest.mark.parametrize(
+        "sampling",
+        [
+            None,
+            ActionDiscretizer.SamplingStrategy.MEDIAN,
+            ActionDiscretizer.SamplingStrategy.LOW,
+            ActionDiscretizer.SamplingStrategy.HIGH,
+            ActionDiscretizer.SamplingStrategy.RANDOM,
+        ],
+    )
+    def test_transform_env(self, envname, interval_as_tensor, categorical, sampling):
+        base_env = GymEnv(
+            HALFCHEETAH_VERSIONED() if envname == "cheetah" else PENDULUM_VERSIONED()
+        )
+        if interval_as_tensor:
+            num_intervals = torch.arange(5, 11 if envname == "cheetah" else 6)
+        else:
+            num_intervals = 5
+        t = ActionDiscretizer(
+            num_intervals=num_intervals,
+            categorical=categorical,
+            sampling=sampling,
+            out_action_key="action_disc",
+        )
+        env = base_env.append_transform(t)
+        check_env_specs(env)
+        r = env.rollout(4)
+        assert r["action"].dtype == torch.float
+        if categorical:
+            assert r["action_disc"].dtype == torch.int64
+        else:
+            assert r["action_disc"].dtype == torch.bool
+        if t.sampling in (
+            t.SamplingStrategy.LOW,
+            t.SamplingStrategy.MEDIAN,
+            t.SamplingStrategy.RANDOM,
+        ):
+            assert (r["action"] < base_env.action_spec.high).all()
+        if t.sampling in (
+            t.SamplingStrategy.HIGH,
+            t.SamplingStrategy.MEDIAN,
+            t.SamplingStrategy.RANDOM,
+        ):
+            assert (r["action"] > base_env.action_spec.low).all()
+
+    def test_transform_model(self):
+        pytest.skip("Tested elsewhere")
+
+    def test_transform_rb(self):
+        pytest.skip("Tested elsewhere")
+
+    def test_transform_inverse(self):
+        pytest.skip("Tested elsewhere")
+
+
 if __name__ == "__main__":
     args, unknown = argparse.ArgumentParser().parse_known_args()
     pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown)

torchrl/data/tensor_specs.py

@@ -3312,6 +3312,8 @@ class MultiDiscreteTensorSpec(DiscreteTensorSpec):
         device (str, int or torch.device, optional): device of
             the tensors.
         dtype (str or torch.dtype, optional): dtype of the tensors.
+        remove_singleton (bool, optional): if ``True``, singleton samples (of size [1])
+            will be squeezed. Defaults to ``True``.
 
     Examples:
         >>> ts = MultiDiscreteTensorSpec((3, 2, 3))
@@ -3330,6 +3332,7 @@ def __init__(
         device: Optional[DEVICE_TYPING] = None,
         dtype: Optional[Union[str, torch.dtype]] = torch.int64,
         mask: torch.Tensor | None = None,
+        remove_singleton: bool = True,
     ):
         if not isinstance(nvec, torch.Tensor):
             nvec = torch.tensor(nvec)
@@ -3354,6 +3357,7 @@ def __init__(
             shape, space, device, dtype, domain="discrete"
         )
         self.update_mask(mask)
+        self.remove_singleton = remove_singleton
 
     def update_mask(self, mask):
         if mask is not None:
@@ -3442,7 +3446,7 @@ def rand(self, shape: Optional[torch.Size] = None) -> torch.Tensor:
                 *self.shape[:-1],
             )
         x = self._rand(space=self.space, shape=shape, i=self.nvec.ndim)
-        if self.shape == torch.Size([1]):
+        if self.remove_singleton and self.shape == torch.Size([1]):
             x = x.squeeze(-1)
         return x
 

torchrl/envs/__init__.py

@@ -38,6 +38,7 @@
 )
 from .model_based import DreamerDecoder, DreamerEnv, ModelBasedEnvBase
 from .transforms import (
+    ActionDiscretizer,
     ActionMask,
     AutoResetEnv,
     AutoResetTransform,

torchrl/envs/transforms/__init__.py

@@ -8,6 +8,7 @@
 from .rb_transforms import MultiStepTransform
 from .rlhf import KLRewardTransform
 from .transforms import (
+    ActionDiscretizer,
     ActionMask,
     AutoResetEnv,
     AutoResetTransform,