Bump version and fix

setup.py

@@ -10,6 +10,7 @@ setup(name='torchy_baselines',
           'gym[classic_control]>=0.11',
           'numpy',
           'torch>=1.4.0',
+          # For saving models
           'cloudpickle',
           # For reading logs
           'pandas',
@@ -31,7 +32,7 @@ setup(name='torchy_baselines',
             # For spelling
             'sphinxcontrib.spelling',
             # Type hints support
-            'sphinx-autodoc-typehints'
+            # 'sphinx-autodoc-typehints'
         ],
         'extra': [
             # For render
@@ -47,7 +48,7 @@ setup(name='torchy_baselines',
       license="MIT",
       long_description="",
       long_description_content_type='text/markdown',
-      version="0.2.3",
+      version="0.2.4",
       )
 
 # python setup.py sdist

torchy_baselines/__init__.py

@@ -4,4 +4,4 @@ from torchy_baselines.ppo import PPO
 from torchy_baselines.sac import SAC
 from torchy_baselines.td3 import TD3
 
-__version__ = "0.2.3"
+__version__ = "0.2.4"

torchy_baselines/common/base_class.py

@@ -176,7 +176,7 @@ class BaseRLModel(ABC):
         low, high = self.action_space.low, self.action_space.high
         return low + (0.5 * (scaled_action + 1.0) * (high - low))
 
-    def _setup_learning_rate(self) -> None:
+    def _setup_lr_schedule(self) -> None:
         """Transform to callable if needed."""
         self.lr_schedule = get_schedule_fn(self.learning_rate)
 

torchy_baselines/ppo/policies.py

@@ -19,7 +19,7 @@ class PPOPolicy(BasePolicy):
 
     :param observation_space: (gym.spaces.Space) Observation space
     :param action_space: (gym.spaces.Space) Action space
-    :param lr_schedule: (callable) Learning rate schedule (could be constant)
+    :param lr_schedule: (Callable) Learning rate schedule (could be constant)
     :param net_arch: ([int or dict]) The specification of the policy and value networks.
     :param device: (str or th.device) Device on which the code should run.
     :param activation_fn: (nn.Module) Activation function

torchy_baselines/ppo/ppo.py

@@ -122,7 +122,7 @@ class PPO(BaseRLModel):
             self._setup_model()
 
     def _setup_model(self) -> None:
-        self._setup_learning_rate()
+        self._setup_lr_schedule()
         # TODO: preprocessing: one hot vector for obs discrete
         state_dim = self.observation_space.shape[0]
         if isinstance(self.action_space, spaces.Box):
@@ -137,7 +137,7 @@ class PPO(BaseRLModel):
         self.rollout_buffer = RolloutBuffer(self.n_steps, state_dim, action_dim, self.device,
                                             gamma=self.gamma, gae_lambda=self.gae_lambda, n_envs=self.n_envs)
         self.policy = self.policy_class(self.observation_space, self.action_space,
-                                        self.learning_rate, use_sde=self.use_sde, device=self.device,
+                                        self.lr_schedule, use_sde=self.use_sde, device=self.device,
                                         **self.policy_kwargs)
         self.policy = self.policy.to(self.device)
 

torchy_baselines/sac/sac.py

@@ -118,7 +118,7 @@ class SAC(OffPolicyRLModel):
             self._setup_model()
 
     def _setup_model(self) -> None:
-        self._setup_learning_rate()
+        self._setup_lr_schedule()
         obs_dim, action_dim = self.observation_space.shape[0], self.action_space.shape[0]
         if self.seed is not None:
             self.set_random_seed(self.seed)

torchy_baselines/td3/td3.py

@@ -118,7 +118,7 @@ class TD3(OffPolicyRLModel):
             self._setup_model()
 
     def _setup_model(self) -> None:
-        self._setup_learning_rate()
+        self._setup_lr_schedule()
         obs_dim, action_dim = self.observation_space.shape[0], self.action_space.shape[0]
         self.set_random_seed(self.seed)
         self.replay_buffer = ReplayBuffer(self.buffer_size, obs_dim, action_dim, self.device)