[Feature] ActionDiscretizer custom sampling

torchrl/envs/transforms/transforms.py

@@ -8583,6 +8583,26 @@ def _indent(s):
             f"\n{_indent(out_action_key)},\n{_indent(sampling)},\n{_indent(categorical)})"
         )
 
+    def _custom_arange(self, nint, device):
+        result = torch.arange(
+            start=0.0,
+            end=1.0,
+            step=1 / nint,
+            dtype=self.dtype,
+            device=device,
+        )
+        result_ = result
+        if self.sampling in (
+            self.SamplingStrategy.HIGH,
+            self.SamplingStrategy.MEDIAN,
+        ):
+            result_ = (1 - result).flip(0)
+        if self.sampling == self.SamplingStrategy.MEDIAN:
+            result = (result + result_) / 2
+        else:
+            result = result_
+        return result
+
     def transform_input_spec(self, input_spec):
         try:
             action_spec = self.parent.full_action_spec_unbatched[self.in_keys_inv[0]]
@@ -8611,29 +8631,11 @@ def transform_input_spec(self, input_spec):
                 num_intervals = int(num_intervals.squeeze())
                 self.num_intervals = torch.as_tensor(num_intervals)
 
-            def custom_arange(nint):
-                result = torch.arange(
-                    start=0.0,
-                    end=1.0,
-                    step=1 / nint,
-                    dtype=self.dtype,
-                    device=action_spec.device,
-                )
-                result_ = result
-                if self.sampling in (
-                    self.SamplingStrategy.HIGH,
-                    self.SamplingStrategy.MEDIAN,
-                ):
-                    result_ = (1 - result).flip(0)
-                if self.sampling == self.SamplingStrategy.MEDIAN:
-                    result = (result + result_) / 2
-                else:
-                    result = result_
-                return result
-
             if isinstance(num_intervals, int):
                 arange = (
-                    custom_arange(num_intervals).expand((*n_act, num_intervals))
+                    self._custom_arange(num_intervals, action_spec.device).expand(
+                        (*n_act, num_intervals)
+                    )
                     * interval
                 )
                 low = action_spec.low
@@ -8642,7 +8644,7 @@ def custom_arange(nint):
                 self.register_buffer("intervals", low + arange)
             else:
                 arange = [
-                    custom_arange(_num_intervals) * interval
+                    self._custom_arange(_num_intervals, action_spec.device) * interval
                     for _num_intervals, interval in zip(
                         num_intervals.tolist(), interval.unbind(-2)
                     )