diff --git a/tests/test_ppo.py b/tests/test_ppo.py
index 17b4b0d..5389370 100644
--- a/tests/test_ppo.py
+++ b/tests/test_ppo.py
@@ -6,4 +6,10 @@ from metastable_baselines2 import PPO
 @pytest.mark.parametrize("env_id", ["LunarLanderContinuous-v2", "MountainCarContinuous-v0"])
 def test_trpl(env_id):
     model = PPO("MlpPolicy", env_id, n_steps=128, seed=0, policy_kwargs=dict(net_arch=[16]), verbose=1)
-    model.learn(total_timesteps=500)
\ No newline at end of file
+    model.learn(total_timesteps=500)
+
+@pytest.mark.parametrize("env_id", ["LunarLanderContinuous-v2"])
+@pytest.mark.parametrize("par_strength", ['DIAG', 'FULL', 'CONT_DIAG', 'CONT_FULL'])
+def test_ppo_pca(env_id, par_strength):
+    model = PPO("MlpPolicy", env_id, n_steps=128, seed=0, use_pca=True, policy_kwargs=dict(net_arch=[16], dist_kwargs={'par_strength': par_strength, 'skip_conditioning': True}), verbose=1)
+    model.learn(total_timesteps=100)
diff --git a/tests/test_trpl.py b/tests/test_trpl.py
index 07e715d..01f6d8d 100644
--- a/tests/test_trpl.py
+++ b/tests/test_trpl.py
@@ -3,7 +3,7 @@ import pytest
 
 from metastable_baselines2 import TRPL
 
-PROJECTIONS = ["Frobenius", "Wasserstein"] #, "KL"]
+PROJECTIONS = ["Frobenius", "Wasserstein"] # KL
 
 @pytest.mark.parametrize("env_id", ["LunarLanderContinuous-v2", "MountainCarContinuous-v0"])
 @pytest.mark.parametrize("projection", PROJECTIONS)
@@ -18,3 +18,10 @@ def test_trpl(env_id, projection):
 def test_trpl_params(env_id, projection, mean_bound, cov_bound):
     model = TRPL("MlpPolicy", env_id, n_steps=128, seed=0, policy_kwargs=dict(net_arch=[16]), projection_class=projection, projection_kwargs={'mean_bound': mean_bound, 'cov_bound': cov_bound}, verbose=1)
     model.learn(total_timesteps=100)
+
+@pytest.mark.parametrize("env_id", ["LunarLanderContinuous-v2"])
+@pytest.mark.parametrize("projection", PROJECTIONS)
+@pytest.mark.parametrize("par_strength", ['DIAG', 'FULL', 'CONT_DIAG', 'CONT_FULL'])
+def test_trpl_pca(env_id, projection, par_strength):
+    model = TRPL("MlpPolicy", env_id, n_steps=128, seed=0, use_pca=True, policy_kwargs=dict(net_arch=[16], dist_kwargs={'par_strength': par_strength, 'skip_conditioning': True}), projection_class=projection, verbose=1)
+    model.learn(total_timesteps=100)