diff --git a/nnabla_rl/model_trainers/policy/ppo_policy_trainer.py b/nnabla_rl/model_trainers/policy/ppo_policy_trainer.py
index 859ea803..cee0863e 100644
--- a/nnabla_rl/model_trainers/policy/ppo_policy_trainer.py
+++ b/nnabla_rl/model_trainers/policy/ppo_policy_trainer.py
@@ -108,10 +108,12 @@ def _build_one_step_graph(self, models: Sequence[Model], training_variables: Tra
             lower_bounds = NF.minimum2(probability_ratio * advantage, clipped_ratio * advantage)
             clip_loss = NF.mean(lower_bounds)
 
-            entropy = distribution.entropy()
-            entropy_loss = NF.mean(entropy)
-
-            self._pi_loss += 0.0 if ignore_loss else (-clip_loss - self._config.entropy_coefficient * entropy_loss)
+            if self._config.entropy_coefficient != 0.0:
+                entropy = distribution.entropy()
+                entropy_loss = NF.mean(entropy)
+                self._pi_loss += 0.0 if ignore_loss else (-clip_loss - self._config.entropy_coefficient * entropy_loss)
+            else:
+                self._pi_loss += 0.0 if ignore_loss else -clip_loss
 
     def _setup_training_variables(self, batch_size) -> TrainingVariables:
         # Training input variables
diff --git a/tests/model_trainers/test_policy_trainers.py b/tests/model_trainers/test_policy_trainers.py
index a05dc4e4..51654d0f 100644
--- a/tests/model_trainers/test_policy_trainers.py
+++ b/tests/model_trainers/test_policy_trainers.py
@@ -24,10 +24,12 @@
 import nnabla.parametric_functions as NPF
 import nnabla_rl.model_trainers as MT
 from nnabla_rl.distributions.gaussian import Gaussian
+from nnabla_rl.distributions.squashed_gaussian import SquashedGaussian
 from nnabla_rl.environments.dummy import DummyContinuous
 from nnabla_rl.environments.environment_info import EnvironmentInfo
 from nnabla_rl.model_trainers.model_trainer import LossIntegration
 from nnabla_rl.model_trainers.policy.dpg_policy_trainer import DPGPolicyTrainer
+from nnabla_rl.model_trainers.policy.ppo_policy_trainer import PPOPolicyTrainer
 from nnabla_rl.model_trainers.policy.soft_policy_trainer import AdjustableTemperature, SoftPolicyTrainer
 from nnabla_rl.model_trainers.policy.trpo_policy_trainer import (
     _concat_network_params_in_ndarray,
@@ -65,11 +67,30 @@ def pi(self, s):
         return s
 
 
+class StochasticNonRnnPolicy(StochasticPolicy):
+    def __init__(self, scope_name: str, squash: bool = False):
+        super().__init__(scope_name)
+        self._squash = squash
+
+    def pi(self, s):
+        if self._squash:
+            return SquashedGaussian(
+                mean=nn.Variable.from_numpy_array(np.zeros(s.shape)),
+                ln_var=nn.Variable.from_numpy_array(np.ones(s.shape)),
+            )
+        else:
+            return Gaussian(
+                mean=nn.Variable.from_numpy_array(np.zeros(s.shape)),
+                ln_var=nn.Variable.from_numpy_array(np.ones(s.shape)),
+            )
+
+
 class StochasticRnnPolicy(StochasticPolicy):
-    def __init__(self, scope_name: str):
+    def __init__(self, scope_name: str, squash: bool = False):
         super().__init__(scope_name)
         self._internal_state_shape = (10,)
         self._fake_internal_state = None
+        self._squash = squash
 
     def is_recurrent(self) -> bool:
         return True
@@ -85,9 +106,16 @@ def get_internal_states(self):
 
     def pi(self, s):
         self._fake_internal_state = self._fake_internal_state * 2
-        return Gaussian(
-            mean=nn.Variable.from_numpy_array(np.zeros(s.shape)), ln_var=nn.Variable.from_numpy_array(np.ones(s.shape))
-        )
+        if self._squash:
+            return SquashedGaussian(
+                mean=nn.Variable.from_numpy_array(np.zeros(s.shape)),
+                ln_var=nn.Variable.from_numpy_array(np.ones(s.shape)),
+            )
+        else:
+            return Gaussian(
+                mean=nn.Variable.from_numpy_array(np.zeros(s.shape)),
+                ln_var=nn.Variable.from_numpy_array(np.ones(s.shape)),
+            )
 
 
 class TrainerTest(metaclass=ABCMeta):
@@ -349,6 +377,43 @@ def test_with_rnn_model(self, unroll_steps, burn_in_steps, loss_integration):
         # pass: If no ecror occurs
 
 
+class TestPPOPolicyTrainer(TrainerTest):
+    def setup_method(self, method):
+        nn.clear_parameters()
+
+    @pytest.mark.parametrize("unroll_steps", [1, 2])
+    @pytest.mark.parametrize("burn_in_steps", [0, 1, 2])
+    @pytest.mark.parametrize("loss_integration", [LossIntegration.LAST_TIMESTEP_ONLY, LossIntegration.ALL_TIMESTEPS])
+    @pytest.mark.parametrize("entropy_coefficient", [0.0, 1.0])
+    @pytest.mark.parametrize("squash", [True, False])
+    def test_with_non_rnn_model(self, unroll_steps, burn_in_steps, loss_integration, entropy_coefficient, squash):
+        env_info = EnvironmentInfo.from_env(DummyContinuous())
+
+        policy = StochasticNonRnnPolicy("stub_pi", squash=squash)
+        config = MT.policy_trainers.PPOPolicyTrainerConfig(
+            unroll_steps=unroll_steps,
+            burn_in_steps=burn_in_steps,
+            loss_integration=loss_integration,
+            entropy_coefficient=entropy_coefficient,
+        )
+        if squash and entropy_coefficient != 0.0:
+            with pytest.raises(NotImplementedError):
+                PPOPolicyTrainer(
+                    policy,
+                    solvers={},
+                    env_info=env_info,
+                    config=config,
+                )
+        else:
+            PPOPolicyTrainer(
+                policy,
+                solvers={},
+                env_info=env_info,
+                config=config,
+            )
+            # pass: If no ecror occurs
+
+
 class TestAdjustableTemperature(TrainerTest):
     def test_initial_temperature(self):
         initial_value = 5.0