[BugFix] Support for tensor collection in the PPOLoss (#2543)

Co-authored-by: Pau Riba <[email protected]>

test/test_cost.py

@@ -7650,6 +7650,7 @@ def _create_mock_actor(
         observation_key="observation",
         sample_log_prob_key="sample_log_prob",
         composite_action_dist=False,
+        aggregate_probabilities=True,
     ):
         # Actor
         action_spec = Bounded(
@@ -7668,7 +7669,7 @@ def _create_mock_actor(
                     "action1": (action_key, "action1"),
                 },
                 log_prob_key=sample_log_prob_key,
-                aggregate_probabilities=True,
+                aggregate_probabilities=aggregate_probabilities,
             )
             module_out_keys = [
                 ("params", "action1", "loc"),
@@ -8038,6 +8039,96 @@ def test_ppo(
         assert counter == 2
         actor.zero_grad()
 
+    @pytest.mark.parametrize("loss_class", (PPOLoss, ClipPPOLoss, KLPENPPOLoss))
+    @pytest.mark.parametrize("gradient_mode", (True, False))
+    @pytest.mark.parametrize("advantage", ("gae", "vtrace", "td", "td_lambda", None))
+    @pytest.mark.parametrize("device", get_default_devices())
+    @pytest.mark.parametrize("td_est", list(ValueEstimators) + [None])
+    @pytest.mark.parametrize("functional", [True, False])
+    def test_ppo_composite_no_aggregate(
+        self, loss_class, device, gradient_mode, advantage, td_est, functional
+    ):
+        torch.manual_seed(self.seed)
+        td = self._create_seq_mock_data_ppo(device=device, composite_action_dist=True)
+
+        actor = self._create_mock_actor(
+            device=device,
+            composite_action_dist=True,
+            aggregate_probabilities=False,
+        )
+        value = self._create_mock_value(device=device)
+        if advantage == "gae":
+            advantage = GAE(
+                gamma=0.9, lmbda=0.9, value_network=value, differentiable=gradient_mode
+            )
+        elif advantage == "vtrace":
+            advantage = VTrace(
+                gamma=0.9,
+                value_network=value,
+                actor_network=actor,
+                differentiable=gradient_mode,
+            )
+        elif advantage == "td":
+            advantage = TD1Estimator(
+                gamma=0.9, value_network=value, differentiable=gradient_mode
+            )
+        elif advantage == "td_lambda":
+            advantage = TDLambdaEstimator(
+                gamma=0.9, lmbda=0.9, value_network=value, differentiable=gradient_mode
+            )
+        elif advantage is None:
+            pass
+        else:
+            raise NotImplementedError
+
+        loss_fn = loss_class(
+            actor,
+            value,
+            loss_critic_type="l2",
+            functional=functional,
+        )
+        if advantage is not None:
+            advantage(td)
+        else:
+            if td_est is not None:
+                loss_fn.make_value_estimator(td_est)
+
+        loss = loss_fn(td)
+        if isinstance(loss_fn, KLPENPPOLoss):
+            kl = loss.pop("kl_approx")
+            assert (kl != 0).any()
+
+        loss_critic = loss["loss_critic"]
+        loss_objective = loss["loss_objective"] + loss.get("loss_entropy", 0.0)
+        loss_critic.backward(retain_graph=True)
+        # check that grads are independent and non null
+        named_parameters = loss_fn.named_parameters()
+        counter = 0
+        for name, p in named_parameters:
+            if p.grad is not None and p.grad.norm() > 0.0:
+                counter += 1
+                assert "actor" not in name
+                assert "critic" in name
+            if p.grad is None:
+                assert ("actor" in name) or ("target_" in name)
+                assert ("critic" not in name) or ("target_" in name)
+        assert counter == 2
+
+        value.zero_grad()
+        loss_objective.backward()
+        counter = 0
+        named_parameters = loss_fn.named_parameters()
+        for name, p in named_parameters:
+            if p.grad is not None and p.grad.norm() > 0.0:
+                counter += 1
+                assert "actor" in name
+                assert "critic" not in name
+            if p.grad is None:
+                assert ("actor" not in name) or ("target_" in name)
+                assert ("critic" in name) or ("target_" in name)
+        assert counter == 2
+        actor.zero_grad()
+
     @pytest.mark.parametrize("loss_class", (PPOLoss, ClipPPOLoss, KLPENPPOLoss))
     @pytest.mark.parametrize("gradient_mode", (True,))
     @pytest.mark.parametrize("device", get_default_devices())

torchrl/objectives/ppo.py

@@ -463,6 +463,8 @@ def reset(self) -> None:
     def get_entropy_bonus(self, dist: d.Distribution) -> torch.Tensor:
         try:
             entropy = dist.entropy()
+            if is_tensor_collection(entropy):
+                entropy = entropy.get(dist.entropy_key)
         except NotImplementedError:
             x = dist.rsample((self.samples_mc_entropy,))
             log_prob = dist.log_prob(x)