Upgrade Stable-Baselines3 (#19)

* Upgrade Stable-Baselines3

* Fix policy saving/loading

docs/misc/changelog.rst

@@ -3,11 +3,12 @@
 Changelog
 ==========
 
-Pre-Release 0.11.0a5 (WIP)
+Pre-Release 0.11.0 (2021-02-27)
 -------------------------------
 
 Breaking Changes:
 ^^^^^^^^^^^^^^^^^
+- Upgraded to Stable-Baselines3 >= 0.11.0
 
 New Features:
 ^^^^^^^^^^^^^

sb3_contrib/qrdqn/policies.py

@@ -68,8 +68,8 @@ def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Ten
         action = q_values.argmax(dim=1).reshape(-1)
         return action
 
-    def _get_data(self) -> Dict[str, Any]:
-        data = super()._get_data()
+    def _get_constructor_parameters(self) -> Dict[str, Any]:
+        data = super()._get_constructor_parameters()
 
         data.update(
             dict(
@@ -176,8 +176,8 @@ def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
     def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
         return self.quantile_net._predict(obs, deterministic=deterministic)
 
-    def _get_data(self) -> Dict[str, Any]:
-        data = super()._get_data()
+    def _get_constructor_parameters(self) -> Dict[str, Any]:
+        data = super()._get_constructor_parameters()
 
         data.update(
             dict(

sb3_contrib/qrdqn/qrdqn.py

@@ -27,13 +27,12 @@ class QRDQN(OffPolicyAlgorithm):
     :param batch_size: Minibatch size for each gradient update
     :param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update
     :param gamma: the discount factor
-    :param train_freq: Update the model every ``train_freq`` steps. Set to `-1` to disable.
+    :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
+        like ``(5, "step")`` or ``(2, "episode")``.
     :param gradient_steps: How many gradient steps to do after each rollout
         (see ``train_freq`` and ``n_episodes_rollout``)
         Set to ``-1`` means to do as many gradient steps as steps done in the environment
         during the rollout.
-    :param n_episodes_rollout: Update the model every ``n_episodes_rollout`` episodes.
-        Note that this cannot be used at the same time as ``train_freq``. Set to `-1` to disable.
     :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
         at a cost of more complexity.
         See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
@@ -66,7 +65,6 @@ def __init__(
         gamma: float = 0.99,
         train_freq: int = 4,
         gradient_steps: int = 1,
-        n_episodes_rollout: int = -1,
         optimize_memory_usage: bool = False,
         target_update_interval: int = 10000,
         exploration_fraction: float = 0.005,
@@ -94,7 +92,6 @@ def __init__(
             gamma,
             train_freq,
             gradient_steps,
-            n_episodes_rollout,
             action_noise=None,  # No action noise
             policy_kwargs=policy_kwargs,
             tensorboard_log=tensorboard_log,

sb3_contrib/tqc/policies.py

@@ -110,8 +110,8 @@ def __init__(
             self.mu = nn.Linear(last_layer_dim, action_dim)
             self.log_std = nn.Linear(last_layer_dim, action_dim)
 
-    def _get_data(self) -> Dict[str, Any]:
-        data = super()._get_data()
+    def _get_constructor_parameters(self) -> Dict[str, Any]:
+        data = super()._get_constructor_parameters()
 
         data.update(
             dict(
@@ -374,8 +374,8 @@ def _build(self, lr_schedule: Callable) -> None:
 
         self.critic.optimizer = self.optimizer_class(critic_parameters, lr=lr_schedule(1), **self.optimizer_kwargs)
 
-    def _get_data(self) -> Dict[str, Any]:
-        data = super()._get_data()
+    def _get_constructor_parameters(self) -> Dict[str, Any]:
+        data = super()._get_constructor_parameters()
 
         data.update(
             dict(

sb3_contrib/tqc/tqc.py

@@ -30,10 +30,9 @@ class TQC(OffPolicyAlgorithm):
     :param batch_size: Minibatch size for each gradient update
     :param tau: the soft update coefficient ("Polyak update", between 0 and 1)
     :param gamma: the discount factor
-    :param train_freq: Update the model every ``train_freq`` steps.
+    :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
+        like ``(5, "step")`` or ``(2, "episode")``.
     :param gradient_steps: How many gradient update after each step
-    :param n_episodes_rollout: Update the model every ``n_episodes_rollout`` episodes.
-        Note that this cannot be used at the same time as ``train_freq``
     :param action_noise: the action noise type (None by default), this can help
         for hard exploration problem. Cf common.noise for the different action noise type.
     :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
@@ -74,7 +73,6 @@ def __init__(
         gamma: float = 0.99,
         train_freq: int = 1,
         gradient_steps: int = 1,
-        n_episodes_rollout: int = -1,
         action_noise: Optional[ActionNoise] = None,
         optimize_memory_usage: bool = False,
         ent_coef: Union[str, float] = "auto",
@@ -105,7 +103,6 @@ def __init__(
             gamma,
             train_freq,
             gradient_steps,
-            n_episodes_rollout,
             action_noise,
             policy_kwargs=policy_kwargs,
             tensorboard_log=tensorboard_log,

sb3_contrib/version.txt

@@ -1 +1 @@
-0.11.0a5
+0.11.0

setup.py

@@ -62,7 +62,7 @@
     packages=[package for package in find_packages() if package.startswith("sb3_contrib")],
     package_data={"sb3_contrib": ["py.typed", "version.txt"]},
     install_requires=[
-        "stable_baselines3[tests,docs]>=0.11.0a2",
+        "stable_baselines3[tests,docs]>=0.11.0",
     ],
     description="Contrib package of Stable Baselines3, experimental code.",
     author="Antonin Raffin",