diff --git a/sheeprl/algos/dreamer_v1/agent.py b/sheeprl/algos/dreamer_v1/agent.py
index a1492103..a654e135 100644
--- a/sheeprl/algos/dreamer_v1/agent.py
+++ b/sheeprl/algos/dreamer_v1/agent.py
@@ -276,14 +276,14 @@ def init_states(self, reset_envs: Optional[Sequence[int]] = None) -> None:
             self.stochastic_state[:, reset_envs] = torch.zeros_like(self.stochastic_state[:, reset_envs])
 
     def get_exploration_actions(
-        self, obs: Tensor, sample_actions: bool = True, mask: Optional[Dict[str, Tensor]] = None, step: int = 0
+        self, obs: Tensor, greedy: bool = False, mask: Optional[Dict[str, Tensor]] = None, step: int = 0
     ) -> Sequence[Tensor]:
         """Return the actions with a certain amount of noise for exploration.
 
         Args:
             obs (Tensor): the current observations.
-            sample_actions (bool): whether or not to sample the actions.
-                Default to True.
+            greedy (bool): whether or not to sample the actions.
+                Default to False.
             mask (Dict[str, Tensor], optional): the action mask (whether or not each action can be executed).
                 Defaults to None.
             step (int): the step of the training, used for the exploration amount.
@@ -292,7 +292,7 @@ def get_exploration_actions(
         Returns:
             The actions the agent has to perform (Sequence[Tensor]).
         """
-        actions = self.get_actions(obs, sample_actions=sample_actions, mask=mask)
+        actions = self.get_actions(obs, greedy=greedy, mask=mask)
         expl_actions = None
         if self.actor._expl_amount > 0:
             expl_actions = self.actor.add_exploration_noise(actions, step=step, mask=mask)
@@ -300,14 +300,14 @@ def get_exploration_actions(
         return expl_actions or actions
 
     def get_actions(
-        self, obs: Tensor, sample_actions: bool = True, mask: Optional[Dict[str, Tensor]] = None
+        self, obs: Tensor, greedy: bool = False, mask: Optional[Dict[str, Tensor]] = None
     ) -> Sequence[Tensor]:
         """Return the greedy actions.
 
         Args:
             obs (Tensor): the current observations.
-            sample_actions (bool): whether or not to sample the actions.
-                Default to True.
+            greedy (bool): whether or not to sample the actions.
+                Default to False.
             mask (Dict[str, Tensor], optional): the action mask (whether or not each action can be executed).
                 Defaults to None.
 
@@ -321,7 +321,7 @@ def get_actions(
         _, self.stochastic_state = compute_stochastic_state(
             self.representation_model(torch.cat((self.recurrent_state, embedded_obs), -1)),
         )
-        actions, _ = self.actor(torch.cat((self.stochastic_state, self.recurrent_state), -1), sample_actions, mask)
+        actions, _ = self.actor(torch.cat((self.stochastic_state, self.recurrent_state), -1), greedy, mask)
         self.actions = torch.cat(actions, -1)
         return actions
 
diff --git a/sheeprl/algos/dreamer_v1/evaluate.py b/sheeprl/algos/dreamer_v1/evaluate.py
index 48845a17..ae16efac 100644
--- a/sheeprl/algos/dreamer_v1/evaluate.py
+++ b/sheeprl/algos/dreamer_v1/evaluate.py
@@ -54,4 +54,4 @@ def evaluate(fabric: Fabric, cfg: Dict[str, Any], state: Dict[str, Any]):
         state["actor"],
     )
     del _
-    test(player, fabric, cfg, log_dir, sample_actions=False)
+    test(player, fabric, cfg, log_dir, greedy=True)
diff --git a/sheeprl/algos/dreamer_v2/agent.py b/sheeprl/algos/dreamer_v2/agent.py
index b7908d4c..0c580c62 100644
--- a/sheeprl/algos/dreamer_v2/agent.py
+++ b/sheeprl/algos/dreamer_v2/agent.py
@@ -503,7 +503,7 @@ def _get_expl_amount(self, step: int) -> Tensor:
         return max(amount, self._expl_min)
 
     def forward(
-        self, state: Tensor, sample_actions: bool = True, mask: Optional[Dict[str, Tensor]] = None
+        self, state: Tensor, greedy: bool = False, mask: Optional[Dict[str, Tensor]] = None
     ) -> Tuple[Sequence[Tensor], Sequence[Distribution]]:
         """
         Call the forward method of the actor model and reorganizes the result with shape (batch_size, *, num_actions),
@@ -511,8 +511,8 @@ def forward(
 
         Args:
             state (Tensor): the current state of shape (batch_size, *, stochastic_size + recurrent_state_size).
-            sample_actions (bool): whether or not to sample the actions.
-                Default to True.
+            greedy (bool): whether or not to sample the actions.
+                Default to False.
             mask (Dict[str, Tensor], optional): the action mask (which actions can be selected).
                 Default to None.
 
@@ -536,7 +536,7 @@ def forward(
                 std = 2 * torch.sigmoid((std + self.init_std) / 2) + self.min_std
                 dist = TruncatedNormal(torch.tanh(mean), std, -1, 1)
                 actions_dist = Independent(dist, 1)
-            if sample_actions:
+            if not greedy:
                 actions = actions_dist.rsample()
             else:
                 sample = actions_dist.sample((100,))
@@ -549,7 +549,7 @@ def forward(
             actions: List[Tensor] = []
             for logits in pre_dist:
                 actions_dist.append(OneHotCategoricalStraightThrough(logits=logits))
-                if sample_actions:
+                if not greedy:
                     actions.append(actions_dist[-1].rsample())
                 else:
                     actions.append(actions_dist[-1].mode)
@@ -608,7 +608,7 @@ def __init__(
         )
 
     def forward(
-        self, state: Tensor, sample_actions: bool = True, mask: Optional[Dict[str, Tensor]] = None
+        self, state: Tensor, greedy: bool = False, mask: Optional[Dict[str, Tensor]] = None
     ) -> Tuple[Sequence[Tensor], Sequence[Distribution]]:
         """
         Call the forward method of the actor model and reorganizes the result with shape (batch_size, *, num_actions),
@@ -616,8 +616,8 @@ def forward(
 
         Args:
             state (Tensor): the current state of shape (batch_size, *, stochastic_size + recurrent_state_size).
-            sample_actions (bool): whether or not to sample the actions.
-                Default to True.
+            greedy (bool): whether or not to sample the actions.
+                Default to False.
             mask (Dict[str, Tensor], optional): the action mask (which actions can be selected).
                 Default to None.
 
@@ -652,7 +652,7 @@ def forward(
                             elif sampled_action == 18:  # Destroy action
                                 logits[t, b][torch.logical_not(mask["mask_destroy"][t, b])] = -torch.inf
             actions_dist.append(OneHotCategoricalStraightThrough(logits=logits))
-            if sample_actions:
+            if not greedy:
                 actions.append(actions_dist[-1].rsample())
             else:
                 actions.append(actions_dist[-1].mode)
@@ -802,7 +802,7 @@ def init_states(self, reset_envs: Optional[Sequence[int]] = None) -> None:
     def get_actions(
         self,
         obs: Dict[str, Tensor],
-        sample_actions: bool = True,
+        greedy: bool = False,
         mask: Optional[Dict[str, Tensor]] = None,
     ) -> Sequence[Tensor]:
         """
@@ -810,8 +810,8 @@ def get_actions(
 
         Args:
             obs (Dict[str, Tensor]): the current observations.
-            sample_actions (bool): whether or not to sample the actions.
-                Default to True.
+            greedy (bool): whether or not to sample the actions.
+                Default to False.
             mask (Dict[str, Tensor], optional): the action mask (which actions can be selected).
                 Default to None.
 
@@ -827,7 +827,7 @@ def get_actions(
         self.stochastic_state = stochastic_state.view(
             *stochastic_state.shape[:-2], self.stochastic_size * self.discrete_size
         )
-        actions, _ = self.actor(torch.cat((self.stochastic_state, self.recurrent_state), -1), sample_actions, mask)
+        actions, _ = self.actor(torch.cat((self.stochastic_state, self.recurrent_state), -1), greedy, mask)
         self.actions = torch.cat(actions, -1)
         return actions
 
diff --git a/sheeprl/algos/dreamer_v2/evaluate.py b/sheeprl/algos/dreamer_v2/evaluate.py
index 145b758d..8b5990b9 100644
--- a/sheeprl/algos/dreamer_v2/evaluate.py
+++ b/sheeprl/algos/dreamer_v2/evaluate.py
@@ -54,4 +54,4 @@ def evaluate(fabric: Fabric, cfg: Dict[str, Any], state: Dict[str, Any]):
         state["actor"],
     )
     del _
-    test(player, fabric, cfg, log_dir, sample_actions=False)
+    test(player, fabric, cfg, log_dir, greedy=True)
diff --git a/sheeprl/algos/dreamer_v2/utils.py b/sheeprl/algos/dreamer_v2/utils.py
index f3a42f39..ed9debb0 100644
--- a/sheeprl/algos/dreamer_v2/utils.py
+++ b/sheeprl/algos/dreamer_v2/utils.py
@@ -108,7 +108,7 @@ def test(
     cfg: Dict[str, Any],
     log_dir: str,
     test_name: str = "",
-    sample_actions: bool = False,
+    greedy: bool = True,
 ):
     """Test the model on the environment with the frozen model.
 
@@ -119,8 +119,8 @@ def test(
         log_dir (str): the logging directory.
         test_name (str): the name of the test.
             Default to "".
-        sample_actoins (bool): whether or not to sample actions.
-            Default to False.
+        greedy (bool): whether or not to sample actions.
+            Default to True.
     """
     env: gym.Env = make_env(cfg, cfg.seed, 0, log_dir, "test" + (f"_{test_name}" if test_name != "" else ""))()
     done = False
@@ -140,7 +140,7 @@ def test(
             elif k in cfg.algo.mlp_keys.encoder:
                 preprocessed_obs[k] = v[None, ...].to(device)
         real_actions = player.get_actions(
-            preprocessed_obs, sample_actions, {k: v for k, v in preprocessed_obs.items() if k.startswith("mask")}
+            preprocessed_obs, greedy, {k: v for k, v in preprocessed_obs.items() if k.startswith("mask")}
         )
         if player.actor.is_continuous:
             real_actions = torch.cat(real_actions, -1).cpu().numpy()
diff --git a/sheeprl/algos/p2e_dv1/evaluate.py b/sheeprl/algos/p2e_dv1/evaluate.py
index 212ead04..4381ae78 100644
--- a/sheeprl/algos/p2e_dv1/evaluate.py
+++ b/sheeprl/algos/p2e_dv1/evaluate.py
@@ -55,4 +55,4 @@ def evaluate(fabric: Fabric, cfg: Dict[str, Any], state: Dict[str, Any]):
         actor_task_state=state["actor_task"],
     )
     del _
-    test(player, fabric, cfg, log_dir, sample_actions=False)
+    test(player, fabric, cfg, log_dir, greedy=True)
diff --git a/sheeprl/algos/p2e_dv2/evaluate.py b/sheeprl/algos/p2e_dv2/evaluate.py
index 8f49205c..5dcb9b15 100644
--- a/sheeprl/algos/p2e_dv2/evaluate.py
+++ b/sheeprl/algos/p2e_dv2/evaluate.py
@@ -55,4 +55,4 @@ def evaluate(fabric: Fabric, cfg: Dict[str, Any], state: Dict[str, Any]):
         actor_task_state=state["actor_task"],
     )
     del _
-    test(player, fabric, cfg, log_dir, sample_actions=False)
+    test(player, fabric, cfg, log_dir, greedy=True)