Fix train_freq at load time (#332)

* Fix train_freq loading

* Update docker

* Add sanity checks + tests for train freq

.gitlab-ci.yml

@@ -1,4 +1,4 @@
-image: stablebaselines/stable-baselines3-cpu:0.11.0a4
+image: stablebaselines/stable-baselines3-cpu:0.11.1
 
 type-check:
   script:

docs/misc/changelog.rst

@@ -3,6 +3,15 @@
 Changelog
 ==========
 
+Pre-Release 0.11.1 (2021-02-27)
+-------------------------------
+
+Bug Fixes:
+^^^^^^^^^^
+- Fixed a bug where ``train_freq`` was not properly converted when loading a saved model
+
+
+
 Pre-Release 0.11.0 (2021-02-27)
 -------------------------------
 

stable_baselines3/common/off_policy_algorithm.py

@@ -131,15 +131,8 @@ def __init__(
         # see https://github.com/hill-a/stable-baselines/issues/863
         self.remove_time_limit_termination = remove_time_limit_termination
 
-        if isinstance(train_freq, int):
-            train_freq = (train_freq, "step")
-
-        try:
-            train_freq = (train_freq[0], TrainFrequencyUnit(train_freq[1]))
-        except ValueError:
-            raise ValueError(f"The unit of the `train_freq` must be either 'step' or 'episode' not '{train_freq[1]}'!")
-
-        self.train_freq = TrainFreq(*train_freq)
+        # Save train freq parameter, will be converted later to TrainFreq object
+        self.train_freq = train_freq
 
         self.actor = None  # type: Optional[th.nn.Module]
         self.replay_buffer = None  # type: Optional[ReplayBuffer]
@@ -149,6 +142,28 @@ def __init__(
         # For gSDE only
         self.use_sde_at_warmup = use_sde_at_warmup
 
+    def _convert_train_freq(self) -> None:
+        """
+        Convert `train_freq` parameter (int or tuple)
+        to a TrainFreq object.
+        """
+        if not isinstance(self.train_freq, TrainFreq):
+            train_freq = self.train_freq
+
+            # The value of the train frequency will be checked later
+            if not isinstance(train_freq, tuple):
+                train_freq = (train_freq, "step")
+
+            try:
+                train_freq = (train_freq[0], TrainFrequencyUnit(train_freq[1]))
+            except ValueError:
+                raise ValueError(f"The unit of the `train_freq` must be either 'step' or 'episode' not '{train_freq[1]}'!")
+
+            if not isinstance(train_freq[0], int):
+                raise ValueError(f"The frequency of `train_freq` must be an integer and not {train_freq[0]}")
+
+            self.train_freq = TrainFreq(*train_freq)
+
     def _setup_model(self) -> None:
         self._setup_lr_schedule()
         self.set_random_seed(self.seed)
@@ -167,6 +182,9 @@ def _setup_model(self) -> None:
         )
         self.policy = self.policy.to(self.device)
 
+        # Convert train freq parameter to TrainFreq object
+        self._convert_train_freq()
+
     def save_replay_buffer(self, path: Union[str, pathlib.Path, io.BufferedIOBase]) -> None:
         """
         Save the replay buffer as a pickle file.

stable_baselines3/version.txt

@@ -1 +1 @@
-0.11.0
+0.11.1

tests/test_run.py

@@ -103,3 +103,39 @@ def test_dqn():
         create_eval_env=True,
     )
     model.learn(total_timesteps=500, eval_freq=250)
+
+
+@pytest.mark.parametrize("train_freq", [4, (4, "step"), (1, "episode")])
+def test_train_freq(tmp_path, train_freq):
+
+    model = SAC(
+        "MlpPolicy",
+        "Pendulum-v0",
+        policy_kwargs=dict(net_arch=[64, 64], n_critics=1),
+        learning_starts=100,
+        buffer_size=10000,
+        verbose=1,
+        train_freq=train_freq,
+    )
+    model.learn(total_timesteps=150)
+    model.save(tmp_path / "test_save.zip")
+    env = model.get_env()
+    model = SAC.load(tmp_path / "test_save.zip", env=env)
+    model.learn(total_timesteps=150)
+    model = SAC.load(tmp_path / "test_save.zip", train_freq=train_freq, env=env)
+    model.learn(total_timesteps=150)
+
+
+@pytest.mark.parametrize("train_freq", ["4", ("1", "episode"), "non_sense", (1, "close")])
+def test_train_freq_fail(train_freq):
+    with pytest.raises(ValueError):
+        model = SAC(
+            "MlpPolicy",
+            "Pendulum-v0",
+            policy_kwargs=dict(net_arch=[64, 64], n_critics=1),
+            learning_starts=100,
+            buffer_size=10000,
+            verbose=1,
+            train_freq=train_freq,
+        )
+        model.learn(total_timesteps=250)

tests/test_save_load.py

@@ -176,7 +176,7 @@ def test_set_env(model_class):
 
     kwargs = {}
     if model_class in {DQN, DDPG, SAC, TD3}:
-        kwargs = dict(learning_starts=100)
+        kwargs = dict(learning_starts=100, train_freq=4)
     elif model_class in {A2C, PPO}:
         kwargs = dict(n_steps=64)
 
@@ -238,12 +238,12 @@ def test_save_load_env_cnn(tmp_path, model_class):
     env = FakeImageEnv(screen_height=40, screen_width=40, n_channels=2, discrete=False)
     kwargs = dict(policy_kwargs=dict(net_arch=[32]))
     if model_class == TD3:
-        kwargs.update(dict(buffer_size=100, learning_starts=50))
+        kwargs.update(dict(buffer_size=100, learning_starts=50, train_freq=4))
 
     model = model_class("CnnPolicy", env, **kwargs).learn(100)
     model.save(tmp_path / "test_save")
     # Test loading with env and continuing training
-    model = model_class.load(str(tmp_path / "test_save.zip"), env=env).learn(100)
+    model = model_class.load(str(tmp_path / "test_save.zip"), env=env, **kwargs).learn(100)
     # clear file from os
     os.remove(tmp_path / "test_save.zip")