Improve coverage

sb3_contrib/tqc/policies.py

@@ -183,18 +183,6 @@ class Actor(BasePolicy):
     def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
         return self.forward(observation, deterministic)
 
-    def evaluate_actions(self, obs: th.Tensor, actions: th.Tensor) -> th.Tensor:
-        """
-        Evaluate actions according to the current policy,
-        given the observations. Only useful when using SDE.
-        :param obs:
-        :param actions:
-        :return: log likelihood of taking those actions
-        """
-        mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
-        self.action_dist.proba_distribution(mean_actions, log_std, **kwargs)
-        return self.action_dist.log_prob(actions)
-
 
 class Critic(BaseModel):
     """

tests/test_run.py

@@ -27,6 +27,8 @@ def test_n_critics(n_critics):
 
 def test_sde():
     model = TQC(
-        "MlpPolicy", "Pendulum-v0", policy_kwargs=dict(net_arch=[64]), use_sde=True, learning_starts=100, verbose=1
+        "MlpPolicy", "Pendulum-v0", policy_kwargs=dict(net_arch=[64], sde_net_arch=[8]), use_sde=True, learning_starts=100, verbose=1
     )
     model.learn(total_timesteps=500)
+    model.policy.reset_noise()
+    model.policy.actor.get_std()

tests/test_save_load.py

@@ -177,17 +177,17 @@ def test_set_env(model_class):
     # create model
     model = model_class("MlpPolicy", env, policy_kwargs=dict(net_arch=[16]))
     # learn
-    model.learn(total_timesteps=500)
+    model.learn(total_timesteps=300)
 
     # change env
     model.set_env(env2)
     # learn again
-    model.learn(total_timesteps=500)
+    model.learn(total_timesteps=300)
 
     # change env test wrapping
     model.set_env(env3)
     # learn again
-    model.learn(total_timesteps=500)
+    model.learn(total_timesteps=300)
 
 
 @pytest.mark.parametrize("model_class", MODEL_LIST)