Docs

benchmarl/algorithms/common.py

@@ -32,7 +32,7 @@ class Algorithm(ABC):
     This should be overridden by implemented algorithms
     and all abstract methods should be implemented.
 
-     Args:
+    Args:
         experiment (Experiment): the experiment class
     """
 
@@ -104,14 +104,13 @@ def _check_specs(self):
     def get_loss_and_updater(self, group: str) -> Tuple[LossModule, TargetNetUpdater]:
         """
         Get the LossModule and TargetNetUpdater for a specific group.
-        This function calls the abstract self._get_loss() which needs to be implemented.
+        This function calls the abstract :class:`~benchmarl.algorithms.Algorithm._get_loss()` which needs to be implemented.
         The function will cache the output at the first call and return the cached values in future calls.
 
         Args:
             group (str): agent group of the loss and updater
 
         Returns: LossModule and TargetNetUpdater for the group
-
         """
         if group not in self._losses_and_updaters.keys():
             action_space = self.action_spec[group, "action"]
@@ -144,7 +143,7 @@ def get_replay_buffer(
     ) -> ReplayBuffer:
         """
         Get the ReplayBuffer for a specific group.
-        This function will check self.on_policy and create the buffer accordingly
+        This function will check ``self.on_policy`` and create the buffer accordingly
 
         Args:
             group (str): agent group of the loss and updater
@@ -165,7 +164,7 @@ def get_replay_buffer(
     def get_policy_for_loss(self, group: str) -> TensorDictModule:
         """
         Get the non-explorative policy for a specific group loss.
-        This function calls the abstract self._get_policy_for_loss() which needs to be implemented.
+        This function calls the abstract :class:`~benchmarl.algorithms.Algorithm._get_policy_for_loss()` which needs to be implemented.
         The function will cache the output at the first call and return the cached values in future calls.
 
         Args:
@@ -192,7 +191,7 @@ def get_policy_for_loss(self, group: str) -> TensorDictModule:
     def get_policy_for_collection(self) -> TensorDictSequential:
         """
         Get the explorative policy for all groups together.
-        This function calls the abstract self._get_policy_for_collection() which needs to be implemented.
+        This function calls the abstract :class:`~benchmarl.algorithms.Algorithm._get_policy_for_collection()` which needs to be implemented.
         The function will cache the output at the first call and return the cached values in future calls.
 
         Returns: TensorDictSequential representing all explorative policies
@@ -217,7 +216,7 @@ def get_policy_for_collection(self) -> TensorDictSequential:
     def get_parameters(self, group: str) -> Dict[str, Iterable]:
         """
         Get the dictionary mapping loss names to the relative parameters to optimize for a given group.
-        This function calls the abstract self._get_parameters() which needs to be implemented.
+        This function calls the abstract :class:`~benchmarl.algorithms.Algorithm._get_parameters()` which needs to be implemented.
 
         Returns: a dictionary mapping loss names to a parameters' list
         """
@@ -332,6 +331,7 @@ class AlgorithmConfig:
     def get_algorithm(self, experiment) -> Algorithm:
         """
         Main function to turn the config into the associated algorithm
+
         Args:
             experiment (Experiment): the experiment class
 
@@ -361,7 +361,7 @@ def get_from_yaml(cls, path: Optional[str] = None):
         Args:
             path (str, optional): The full path of the yaml file to load from.
                 If None, it will default to
-                benchmarl/conf/algorithm/self.associated_class().__name__
+                ``benchmarl/conf/algorithm/self.associated_class().__name__``
 
         Returns: the loaded AlgorithmConfig
         """

benchmarl/algorithms/iddpg.py

@@ -19,6 +19,16 @@
 
 
 class Iddpg(Algorithm):
+    """Same as :class:`~benchmarkl.algorithms.Maddpg` (from `https://arxiv.org/abs/1706.02275 <https://arxiv.org/abs/1706.02275>`__) but with decentralized critics.
+
+    Args:
+        share_param_critic (bool): Whether to share the parameters of the critics withing agent groups
+        loss_function (str): loss function for the value discrepancy. Can be one of "l1", "l2" or "smooth_l1".
+        delay_value (bool): whether to separate the target value networks from the value networks used for
+            data collection.
+
+    """
+
     def __init__(
         self, share_param_critic: bool, loss_function: str, delay_value: bool, **kwargs
     ):
@@ -227,6 +237,8 @@ def get_value_module(self, group: str) -> TensorDictModule:
 
 @dataclass
 class IddpgConfig(AlgorithmConfig):
+    """Configuration dataclass for :class:`~benchmarkl.algorithms.Iddpg`."""
+
     share_param_critic: bool = MISSING
     loss_function: str = MISSING
     delay_value: bool = MISSING

benchmarl/algorithms/ippo.py

@@ -22,6 +22,21 @@
 
 
 class Ippo(Algorithm):
+    """Independent PPO (from `https://arxiv.org/abs/2011.09533 <https://arxiv.org/abs/2011.09533>`__).
+
+    Args:
+        share_param_critic (bool): Whether to share the parameters of the critics withing agent groups
+        clip_epsilon (scalar): weight clipping threshold in the clipped PPO loss equation.
+        entropy_coef (scalar): entropy multiplier when computing the total loss.
+        critic_coef (scalar): critic loss multiplier when computing the total
+        loss_critic_type (str): loss function for the value discrepancy.
+            Can be one of "l1", "l2" or "smooth_l1".
+        lmbda (float): The GAE lambda
+        scale_mapping (str): positive mapping function to be used with the std.
+            choices: "softplus", "exp", "relu", "biased_softplus_1";
+
+    """
+
     def __init__(
         self,
         share_param_critic: bool,
@@ -270,6 +285,8 @@ def get_critic(self, group: str) -> TensorDictModule:
 
 @dataclass
 class IppoConfig(AlgorithmConfig):
+    """Configuration dataclass for :class:`~benchmarkl.algorithms.Ippo`."""
+
     share_param_critic: bool = MISSING
     clip_epsilon: float = MISSING
     entropy_coef: float = MISSING

benchmarl/algorithms/iql.py

@@ -18,6 +18,15 @@
 
 
 class Iql(Algorithm):
+    """Independent Q Learning (from `https://www.semanticscholar.org/paper/Multi-Agent-Reinforcement-Learning%3A-Independent-Tan/59de874c1e547399b695337bcff23070664fa66e <https://www.semanticscholar.org/paper/Multi-Agent-Reinforcement-Learning%3A-Independent-Tan/59de874c1e547399b695337bcff23070664fa66e>`__).
+
+    Args:
+        loss_function (str): loss function for the value discrepancy. Can be one of "l1", "l2" or "smooth_l1".
+        delay_value (bool): whether to separate the target value networks from the value networks used for
+            data collection.
+
+    """
+
     def __init__(self, delay_value: bool, loss_function: str, **kwargs):
         super().__init__(**kwargs)
 
@@ -175,6 +184,8 @@ def process_batch(self, group: str, batch: TensorDictBase) -> TensorDictBase:
 
 @dataclass
 class IqlConfig(AlgorithmConfig):
+    """Configuration dataclass for :class:`~benchmarkl.algorithms.Iql`."""
+
     delay_value: bool = MISSING
     loss_function: str = MISSING
 

benchmarl/algorithms/isac.py

@@ -26,6 +26,24 @@
 
 
 class Isac(Algorithm):
+    """Independent Soft Actor Critic.
+
+    Args:
+        share_param_critic (bool): Whether to share the parameters of the critics withing agent groups
+        num_qvalue_nets (integer): number of Q-Value networks used.
+        loss_function (str): loss function to be used with
+            the value function loss.
+        delay_qvalue ():
+        target_entropy ():
+        discrete_target_entropy_weight ():
+        alpha_init ():
+        min_alpha ():
+        max_alpha ():
+        fixed_alpha ():
+        scale_mapping ():
+
+    """
+
     def __init__(
         self,
         share_param_critic: bool,

docs/source/_templates/autosummary/class_private.rst

@@ -0,0 +1,9 @@
+{{ fullname | escape | underline }}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+   :show-inheritance:
+   :members:
+   :undoc-members:
+   :private-members:

docs/source/modules/algorithms.rst

@@ -15,7 +15,7 @@ Common
 .. autosummary::
    :nosignatures:
    :toctree: ../generated
-   :template: autosummary/class.rst
+   :template: autosummary/class_private.rst
 
    Algorithm
    AlgorithmConfig
@@ -26,7 +26,7 @@ Algorithms
 .. autosummary::
    :nosignatures:
    :toctree: ../generated
-   :template: autosummary/class.rst
+   :template: autosummary/class_private.rst
 
    {% for name in benchmarl.algorithms.classes %}
      {{ name }}