polish(pu): polish comments in a2c/bcq/fqf/ibc policy

ding/example/bcq.py

@@ -15,7 +15,7 @@
 
 def main():
     # If you don't have offline data, you need to prepare if first and set the data_path in config
-    # For demostration, we also can train a RL policy (e.g. SAC) and collect some data
+    # For demonstration, we also can train a RL policy (e.g. SAC) and collect some data
     logging.getLogger().setLevel(logging.INFO)
     cfg = compile_config(main_config, create_cfg=create_config, auto=True)
     ding_init(cfg)

ding/policy/acer.py

@@ -47,11 +47,11 @@ class ACERPolicy(Policy):
     config = dict(
         type='acer',
         cuda=False,
-        # (bool) whether use on-policy training pipeline(behaviour policy and training policy are the same)
+        # (bool) whether to use on-policy training pipeline (behaviour policy and training policy are the same)
         # here we follow ppo serial pipeline, the original is False
         on_policy=False,
         priority=False,
-        # (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.
+        # (bool) Whether to use Importance Sampling Weight to correct biased update. If True, priority must be True.
         priority_IS_weight=False,
         learn=dict(
             # (str) the type of gradient clip method
@@ -295,7 +295,7 @@ def _reshape_data(
             Update values and rewards with the weight
         Arguments:
             - output (:obj:`Dict[int, Any]`): Dict type data, output of learn_model forward. \
-             Values are torch.Tensor or np.ndarray or dict/list combinations,keys are value, logit.
+             Values are torch.Tensor or np.ndarray or dict/list combinations, keys are value, logit.
             - data (:obj:`Dict[int, Any]`): Dict type data, input of policy._forward_learn \
              Values are torch.Tensor or np.ndarray or dict/list combinations. Keys includes at \
              least ['logit', 'action', 'reward', 'done',]
@@ -378,7 +378,7 @@ def _forward_collect(self, data: Dict[int, Any]) -> Dict[int, Dict[str, Any]]:
             action, values are torch.Tensor or np.ndarray or dict/list combinations,keys \
             are env_id indicated by integer.
         Returns:
-            - output (:obj:`Dict[int, Dict[str,Any]]`): Dict of predicting policy_output(logit, action) for each env.
+            - output (:obj:`Dict[int, Dict[str, Any]]`): Dict of predicting policy_output(logit, action) for each env.
         ReturnsKeys
             - necessary: ``logit``, ``action``
         """
@@ -479,7 +479,7 @@ def _monitor_vars_learn(self) -> List[str]:
         Returns:
             - model_info (:obj:`Tuple[str, List[str]]`): model name and mode import_names
         .. note::
-            The user can define and use customized network model but must obey the same interface definition indicated \
-            by import_names path. For IMPALA, ``ding.model.interface.IMPALA``
+            The user can define and use a customized network model but must obey the same interface definition \
+            indicated by import_names path. For IMPALA, ``ding.model.interface.IMPALA``
         """
         return ['actor_loss', 'bc_loss', 'policy_loss', 'critic_loss', 'entropy_loss', 'kl_div']

ding/policy/base_policy.py

@@ -196,14 +196,14 @@ def hook(*ignore):
     def _create_model(self, cfg: EasyDict, model: Optional[torch.nn.Module] = None) -> torch.nn.Module:
         """
         Overview:
-            Create or validate the neural network model according to input configures and model. If the input model is \
-            None, then the model will be created according to ``default_model`` method and ``cfg.model`` field. \
-            Otherwise, the model will be verified as an instance of ``torch.nn.Module`` and set to the ``model`` \
-            instance created by outside caller.
+            Create or validate the neural network model according to the input configuration and model. \
+            If the input model is None, then the model will be created according to ``default_model`` \
+            method and ``cfg.model`` field. Otherwise, the model will be verified as an instance of \
+            ``torch.nn.Module`` and set to the ``model`` instance created by outside caller.
         Arguments:
             - cfg (:obj:`EasyDict`): The final merged config used to initialize policy.
             - model (:obj:`torch.nn.Module`): The neural network model used to initialize policy. User can refer to \
-                the default model defined in corresponding policy to customize its own model.
+                the default model defined in the corresponding policy to customize its own model.
         Returns:
             - model (:obj:`torch.nn.Module`): The created neural network model. The different modes of policy will \
                 add distinct wrappers and plugins to the model, which is used to train, collect and evaluate.
@@ -272,7 +272,7 @@ def _init_eval(self) -> None:
         Overview:
             Initialize the eval mode of policy, including related attributes and modules. This method will be \
             called in ``__init__`` method if ``eval`` field is in ``enable_field``. Almost different policies have \
-            its own eval mode, so this method must be overrided in subclass.
+            its own eval mode, so this method must be override in subclass.
 
         .. note::
             For the member variables that need to be saved and loaded, please refer to the ``_state_dict_eval`` \
@@ -289,7 +289,7 @@ def learn_mode(self) -> 'Policy.learn_function':  # noqa
         """
         Overview:
             Return the interfaces of learn mode of policy, which is used to train the model. Here we use namedtuple \
-            to define immutable interfaces and restrict the usage of policy in different mode. Moreover, derived \
+            to define immutable interfaces and restrict the usage of policy in different modes. Moreover, derived \
             subclass can override the interfaces to customize its own learn mode.
         Returns:
             - interfaces (:obj:`Policy.learn_function`): The interfaces of learn mode of policy, it is a namedtuple \
@@ -316,7 +316,7 @@ def collect_mode(self) -> 'Policy.collect_function':  # noqa
         """
         Overview:
             Return the interfaces of collect mode of policy, which is used to train the model. Here we use namedtuple \
-            to define immutable interfaces and restrict the usage of policy in different mode. Moreover, derived \
+            to define immutable interfaces and restrict the usage of policy in different modes. Moreover, derived \
             subclass can override the interfaces to customize its own collect mode.
         Returns:
             - interfaces (:obj:`Policy.collect_function`): The interfaces of collect mode of policy, it is a \
@@ -370,7 +370,7 @@ def _set_attribute(self, name: str, value: Any) -> None:
         Overview:
             In order to control the access of the policy attributes, we expose different modes to outside rather than \
             directly use the policy instance. And we also provide a method to set the attribute of the policy in \
-            different modes. And the new attribute will named as ``_{name}``.
+            different modes. And the new attribute will name as ``_{name}``.
         Arguments:
             - name (:obj:`str`): The name of the attribute.
             - value (:obj:`Any`): The value of the attribute.
@@ -416,7 +416,7 @@ def sync_gradients(self, model: torch.nn.Module) -> None:
             - model (:obj:`torch.nn.Module`): The model to synchronize gradients.
 
         .. note::
-            This method is only used in multi-gpu training, and it shoule be called after ``backward`` method and \
+            This method is only used in multi-gpu training, and it should be called after ``backward`` method and \
             before ``step`` method. The user can also use ``bp_update_sync`` config to control whether to synchronize \
             gradients allreduce and optimizer updates.
         """