[BugFix] Update cql docstring example (#1951)

Co-authored-by: Vincent Moens <[email protected]>

Author: BY571

torchrl/objectives/cql.py

@@ -45,6 +45,7 @@ class CQLLoss(LossModule):
         actor_network (ProbabilisticActor): stochastic actor
         qvalue_network (TensorDictModule): Q(s, a) parametric model.
             This module typically outputs a ``"state_action_value"`` entry.
+
     Keyword args:
         loss_function (str, optional): loss function to be used with
             the value function loss. Default is `"smooth_l1"`.
@@ -127,8 +128,9 @@ class CQLLoss(LossModule):
                 alpha: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                 entropy: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                 loss_actor: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                loss_actor_bc: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                 loss_alpha: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
-                loss_alpha_prime: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                loss_cql: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                 loss_qvalue: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
             batch_size=torch.Size([]),
             device=None,
@@ -169,10 +171,10 @@ class CQLLoss(LossModule):
         >>> qvalue = ValueOperator(
         ...     module=module,
         ...     in_keys=['observation', 'action'])
-        >>> loss = CQLLoss(actor, qvalue, value)
+        >>> loss = CQLLoss(actor, qvalue)
         >>> batch = [2, ]
         >>> action = spec.rand(batch)
-        >>> loss_actor, loss_qvalue, _, _, _, _ = loss(
+        >>> loss_actor, loss_actor_bc, loss_qvalue, loss_cql, *_ = loss(
         ...     observation=torch.randn(*batch, n_obs),
         ...     action=action,
         ...     next_done=torch.zeros(*batch, 1, dtype=torch.bool),
@@ -185,7 +187,7 @@ class CQLLoss(LossModule):
     method.
 
     Examples:
-        >>> loss.select_out_keys('loss_actor', 'loss_qvalue')
+        >>> _ = loss.select_out_keys('loss_actor', 'loss_qvalue')
         >>> loss_actor, loss_qvalue = loss(
         ...     observation=torch.randn(*batch, n_obs),
         ...     action=action,
@@ -471,10 +473,11 @@ def out_keys(self):
                 "loss_qvalue",
                 "loss_cql",
                 "loss_alpha",
-                "loss_alpha_prime",
                 "alpha",
                 "entropy",
             ]
+            if self.with_lagrange:
+                keys.append("loss_alpha_prime")
             self._out_keys = keys
         return self._out_keys
 
@@ -876,8 +879,9 @@ class DiscreteCQLLoss(LossModule):
 
 
     Examples:
-        >>> from torchrl.modules import MLP
+        >>> from torchrl.modules import MLP, QValueActor
         >>> from torchrl.data import OneHotDiscreteTensorSpec
+        >>> from torchrl.objectives import DiscreteCQLLoss
         >>> n_obs, n_act = 4, 3
         >>> value_net = MLP(in_features=n_obs, out_features=n_act)
         >>> spec = OneHotDiscreteTensorSpec(n_act)
@@ -895,8 +899,11 @@ class DiscreteCQLLoss(LossModule):
         >>> loss(data)
         TensorDict(
             fields={
-                loss: Tensor(shape=torch.Size([]), device=cuda:0, dtype=torch.float32, is_shared=True),
-                loss_cql: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
+                loss_cql: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                loss_qvalue: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                pred_value: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                target_value: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                td_error: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False)},
             batch_size=torch.Size([]),
             device=None,
             is_shared=False)