Minor_edits_doc

docs/quickstart.rst

@@ -56,8 +56,8 @@ sure you look at the examples
     hive_single_agent_loop -c <config-file>
     hive_multi_agent_loop -c <config-file>
 
-Finally, if instead you want to use your own custom custom components you can
-simply register it with RLHive and run your config normally: 
+Finally, if instead you want to use your own custom components you can
+simply register it with RLHive and run your config in the following way: 
 
 .. code-block:: python
 

docs/tutorials/agent_tutorial.rst

@@ -32,6 +32,7 @@ First, we define the constructor:
             self._q_values = np.zeros(obs_dim, act_dim)
             self._gamma = gamma
             self._alpha = alpha
+            self._act_dim = act_dim
             self._epsilon_schedule = LinearSchedule(1.0, final_epsilon, explore_steps)
 
 In this constructor, we created a numpy array to keep track of the Q-values for every

docs/tutorials/configuration_tutorial.rst

@@ -36,8 +36,8 @@ In this example, :py:class:`~hive.agents.dqn_agent.DQNAgent` ,
 :py:class:`~hive.agents.qnets.mlp.MLPNetwork` , and
 :py:class:`~hive.replays.circular_replay.CircularReplayBuffer` are all classes
 registered with RLHive. Thus, we can do this configuration directly. When the
-``registry`` getter function for agents, 
-:py:meth:`~hive.utils.registry.Registry.get_agent` is then called with this config
+``registry`` getter function for agents 
+:py:meth:`~hive.utils.registry.Registry.get_agent`, is then called with this config
 dictionary (with the missing required arguments such as ``obs_dim`` and ``act_dim``,
 filled in), it will build all the inner RLHive objects automatically.
 This works by using the type annotations on the constructors of the objects, so
@@ -51,8 +51,8 @@ Overriding from command lines
 --------------------------------
 When using the ``registry`` getter functions, RLHive automatically checks any command 
 line arguments passed to see if they match/override any default or yaml configured 
-arguments. With ``getter`` functionyou provide a config and a prefix. That prefix
-is added prepended to any argument names when searching the command line. For example,
+arguments. With ``getter`` function you provide a config and a prefix. That prefix
+is added, prepended to any argument names when searching the command line. For example,
 with the above config, if it were loaded and the 
 :py:meth:`~hive.utils.registry.Registry.get_agent` method was called as follows:
 
@@ -65,7 +65,7 @@ python script: ``--ag.discount_rate .95``. This can go arbitrarily deep into reg
 RLHive class. For example, if you wanted to change the capacity of the replay buffer,
 you could pass ``--ag.replay_buffer.capacity 100000``.
 
-If the type annotation the argument ``arg`` is ``List[C]`` where C is a registered
+If the type annotation of the argument ``arg`` is ``List[C]`` where C is a registered
 RLHive class, then you can override the argument of an individual object, ``foo``,
 configured through YAML by passing ``--arg.0.foo <value>``.
 

docs/tutorials/env_tutorial.rst

@@ -22,7 +22,7 @@ Creating an Environment
 
 RLHive Environments
 ^^^^^^^^^^^^^^^^^^^
-Every environment used in RLHive should be a subclass of `~hive.envs.base.BaseEnv`.
+Every environment used in RLHive should be a subclass of :py:class:`~hive.envs.base.BaseEnv`.
 It should provide a ``reset`` function that resets the environment to a new episode 
 and returns a tuple of ``(observation, turn)`` and a ``step`` function that takes in
 an action, performs the step in the environment, and returns a tuple of 

docs/tutorials/runner_tutorial.rst

@@ -6,7 +6,7 @@ We provide two different :py:class:`~hive.runners.base.Runner` classes:
 for both Runner classes can be viewed in their respective files with the
 :py:meth:`set_up_experiment` functions. 
 The :py:meth:`~hive.utils.registry.get_parsed_args` function can be used
-to get any arguments from the command line are not part of the signatures
+to get any arguments from the command line that are not part of the signatures
 of already registered RLHive class constructors. 
 
 

hive/agents/ddpg.py

@@ -50,18 +50,18 @@ def __init__(
                 None, defaults to :py:class:`~torch.nn.Identity`.
             actor_net (FunctionApproximator): The network that takes the encoded
                 observations from representation_net and outputs the representations
-                used to compute the actions (ie everything except the last layer).
+                used to compute the actions (i.e. everything except the last layer).
             critic_net (FunctionApproximator): The network that takes two inputs: the
                 encoded observations from representation_net and actions. It outputs
-                the representations used to compute the values of the actions (ie
+                the representations used to compute the values of the actions (i.e.
                 everything except the last layer).
             init_fn (InitializationFn): Initializes the weights of agent networks using
                 create_init_weights_fn.
             actor_optimizer_fn (OptimizerFn): A function that takes in the list of
-                parameters of the actor returns the optimizer for the actor. If None,
+                parameters of the actor and returns the optimizer for the actor. If None,
                 defaults to :py:class:`~torch.optim.Adam`.
             critic_optimizer_fn (OptimizerFn): A function that takes in the list of
-                parameters of the critic returns the optimizer for the critic. If None,
+                parameters of the critic and returns the optimizer for the critic. If None,
                 defaults to :py:class:`~torch.optim.Adam`.
             critic_loss_fn (LossFn): The loss function used to optimize the critic. If
                 None, defaults to :py:class:`~torch.nn.MSELoss`.

hive/agents/rainbow.py

@@ -257,6 +257,7 @@ def act(self, observation):
     def update(self, update_info):
         """
         Updates the DQN agent.
+
         Args:
             update_info: dictionary containing all the necessary information to
             update the agent. Should contain a full transition, with keys for

hive/agents/td3.py

@@ -62,10 +62,10 @@ def __init__(
                 None, defaults to :py:class:`~torch.nn.Identity`.
             actor_net (FunctionApproximator): The network that takes the encoded
                 observations from representation_net and outputs the representations
-                used to compute the actions (ie everything except the last layer).
+                used to compute the actions (i.e. everything except the last layer).
             critic_net (FunctionApproximator): The network that takes two inputs: the
                 encoded observations from representation_net and actions. It outputs
-                the representations used to compute the values of the actions (ie
+                the representations used to compute the values of the actions (i.e.
                 everything except the last layer).
             init_fn (InitializationFn): Initializes the weights of agent networks using
                 create_init_weights_fn.

hive/replays/circular_replay.py

@@ -43,9 +43,6 @@ def __init__(
                 a numpy type, a string of the form np.uint8 or numpy.uint8 is
                 acceptable.
             action_shape: Shape of actions that will be stored in the buffer.
-            action_dtype: Type of actions that will be stored in the buffer. Format is
-                described in the description of observation_dtype.
-            action_shape: Shape of actions that will be stored in the buffer.
             action_dtype: Type of actions that will be stored in the buffer. Format is
                 described in the description of observation_dtype.
             reward_shape: Shape of rewards that will be stored in the buffer.

hive/replays/prioritized_replay.py

@@ -46,9 +46,6 @@ def __init__(
                 a numpy type, a string of the form np.uint8 or numpy.uint8 is
                 acceptable.
             action_shape: Shape of actions that will be stored in the buffer.
-            action_dtype: Type of actions that will be stored in the buffer. Format is
-                described in the description of observation_dtype.
-            action_shape: Shape of actions that will be stored in the buffer.
             action_dtype: Type of actions that will be stored in the buffer. Format is
                 described in the description of observation_dtype.
             reward_shape: Shape of rewards that will be stored in the buffer.

hive/runners/utils.py

@@ -17,7 +17,7 @@ def load_config(
     logger_config=None,
 ):
     """Used to load config for experiments. Agents, environment, and loggers components
-    in main config file can be overrided based on other log files.
+    in main config file can be overriden based on other log files.
 
     Args:
         config (str): Path to configuration file. Either this or :obj:`preset_config`

hive/utils/experiment.py

@@ -72,6 +72,7 @@ def should_save(self):
 
     def save(self, tag="current"):
         """Saves the experiment.
+
         Args:
             tag (str): Tag to prefix the folder.
         """

hive/utils/registry.py

@@ -40,7 +40,7 @@ class Registry:
     For example, let's consider the following scenario:
     Your agent class has an argument `arg1` which is annotated to be `List[Class1]`,
     `Class1` is `Registrable`, and the `Class1` constructor takes an argument `arg2`.
-    In the passed yml config, there are two different Class1 object configs listed.
+    In the passed yml config, there are two different Class1 object configs listed,
     the constructor will check to see if both `--agent.arg1.0.arg2` and
     `--agent.arg1.1.arg2` have been passed.