diff --git a/torchrl/trainers/helpers/losses.py b/torchrl/trainers/helpers/losses.py
index 92e304a895f..f4ae7a9a842 100644
--- a/torchrl/trainers/helpers/losses.py
+++ b/torchrl/trainers/helpers/losses.py
@@ -222,13 +222,33 @@ def make_ppo_loss(model, cfg) -> PPOLoss:
         )
     else:
         advantage = None
-    loss_module = loss_dict[cfg.loss](
-        actor=actor_model,
-        critic=critic_model,
-        advantage_module=advantage,
-        loss_critic_type=cfg.loss_function,
-        entropy_coef=cfg.entropy_coef,
-    )
+
+    kwargs = {
+        "actor": actor_model,
+        "critic": critic_model,
+        "advantage_module": advantage,
+        "loss_critic_type": cfg.loss_function,
+        "entropy_coef": cfg.entropy_coef,
+    }
+
+    if cfg.loss == "clip":
+        kwargs.update(
+            {
+                "clip_epsilon": cfg.clip_epsilon,
+            }
+        )
+    elif cfg.loss == "kl":
+        kwargs.update(
+            {
+                "dtarg": cfg.dtarg,
+                "beta": cfg.beta,
+                "increment": cfg.increment,
+                "decrement": cfg.decrement,
+                "samples_mc_kl": cfg.samples_mc_kl,
+            }
+        )
+
+    loss_module = loss_dict[cfg.loss](**kwargs)
     return loss_module
 
 
@@ -279,13 +299,37 @@ class PPOLossConfig:
 
     loss: str = "clip"
     # PPO loss class, either clip or kl or base/<empty>. Default=clip
+
+    # PPOLoss base parameters:
     gamma: float = 0.99
     # Decay factor for return computation. Default=0.99.
     lmbda: float = 0.95
     # lambda factor in GAE (using 'lambda' as attribute is prohibited in python, hence the misspelling)
+    entropy_bonus: bool = True
+    # Whether or not to add an entropy term to the PPO loss.
     entropy_coef: float = 1e-3
     # Entropy factor for the PPO loss
+    samples_mc_entropy: int = 1
+    # Number of samples to use for a Monte-Carlo estimate if the policy distribution has not closed formula.
     loss_function: str = "smooth_l1"
     # loss function for the value network. Either one of l1, l2 or smooth_l1 (default).
     advantage_in_loss: bool = False
-    # if True, the advantage is computed on the sub-batch.
+    # if True, the advantage is computed on the sub-batch.,
+    critic_coef: float = 1.0
+    # Critic loss multiplier when computing the total loss.
+
+    # ClipPPOLoss parameters:
+    clip_epsilon: float = 0.2
+    # weight clipping threshold in the clipped PPO loss equation.
+
+    # KLPENPPOLoss parameters:
+    dtarg: float = 0.01
+    # target KL divergence.
+    beta: float = 1.0
+    # initial KL divergence multiplier.
+    increment: float = 2
+    # how much beta should be incremented if KL > dtarg. Valid range: increment >= 1.0
+    decrement: float = 0.5
+    # how much beta should be decremented if KL < dtarg. Valid range: decrement <= 1.0
+    samples_mc_kl: int = 1
+    # Number of samples to use for a Monte-Carlo estimate of KL if necessary