Remove arguments already handled by **kwargs from environment configu…

…rations.

PiperOrigin-RevId: 681502774
Change-Id: I4277ec049c072c8fdfdde3148918c659df0bfd19

examples/modular/environment/haggling_gullible.py

@@ -14,62 +14,25 @@
 
 """A Concordia Environment Configuration."""
 
-from collections.abc import Callable, Sequence
-import types
-
 from examples.modular.environment import haggling
-from concordia.factory.agent import basic_agent
-from concordia.language_model import language_model
-from concordia.utils import measurements as measurements_lib
-import numpy as np
 
 
 class Simulation(haggling.Simulation):
   """Simulation with pub closures."""
 
   def __init__(
       self,
-      *,
-      model: language_model.LanguageModel,
-      embedder: Callable[[str], np.ndarray],
-      measurements: measurements_lib.Measurements,
-      agent_module: types.ModuleType = basic_agent,
-      resident_visitor_modules: Sequence[types.ModuleType] = (),
-      supporting_agent_module: types.ModuleType | None = None,
-      time_and_place_module: str | None = None,
       **kwargs,
   ):
     """Initialize the simulation object.
 
     The launch script assumes this API object has a run() method.
 
     Args:
-      model: the language model to use.
-      embedder: the sentence transformer to use.
-      measurements: the measurements object to use.
-      agent_module: the agent module to use for all main characters.
-      resident_visitor_modules: optionally, use different modules for majority
-        and minority parts of the focal population.
-      supporting_agent_module: agent module to use for all supporting players. A
-        supporting player is a non-player character with a persistent memory
-        that plays a specific role in defining the task environment. Their role
-        is not incidental but rather is critcal to making the task what it is.
-        Supporting players are not necessarily interchangeable with focal or
-        background players.
-      time_and_place_module: optionally, specify a module containing settings
-        that create a sense of setting in a specific time and place. If not
-        specified, a random module will be chosen from the default options.
-      **kwargs: additional arguments to pass to the superclass constructor.
+      **kwargs: arguments to pass to the base class.
     """
 
     super().__init__(
-        model=model,
-        embedder=embedder,
-        measurements=measurements,
-        agent_module=agent_module,
-        resident_visitor_modules=resident_visitor_modules,
-        supporting_agent_module=supporting_agent_module,
-        time_and_place_module=time_and_place_module,
         num_supporting_player=1,
         only_match_with_support=True,
         **kwargs,

examples/modular/environment/pub_coordination_closures.py

@@ -14,61 +14,25 @@
 
 """A Concordia Environment Configuration."""
 
-from collections.abc import Callable, Sequence
-import types
-
 from examples.modular.environment import pub_coordination
-from concordia.factory.agent import basic_agent
-from concordia.language_model import language_model
-from concordia.utils import measurements as measurements_lib
-import numpy as np
 
 
 class Simulation(pub_coordination.Simulation):
   """Simulation with pub closures."""
 
   def __init__(
       self,
-      model: language_model.LanguageModel,
-      embedder: Callable[[str], np.ndarray],
-      measurements: measurements_lib.Measurements,
-      agent_module: types.ModuleType = basic_agent,
-      resident_visitor_modules: Sequence[types.ModuleType] | None = None,
-      supporting_agent_module: types.ModuleType | None = None,
-      time_and_place_module: str | None = None,
       **kwargs,
   ):
     """Initialize the simulation object.
 
     The launch script assumes this API object has a run() method.
 
     Args:
-      model: the language model to use.
-      embedder: the sentence transformer to use.
-      measurements: the measurements object to use.
-      agent_module: the agent module to use for all main characters.
-      resident_visitor_modules: optionally, use different modules for majority
-        and minority parts of the focal population.
-      supporting_agent_module: agent module to use for all supporting players. A
-        supporting player is a non-player character with a persistent memory
-        that plays a specific role in defining the task environment. Their role
-        is not incidental but rather is critcal to making the task what it is.
-        Supporting players are not necessarily interchangeable with focal or
-        background players.
-      time_and_place_module: optionally, specify a module containing settings
-        that create a sense of setting in a specific time and place. If not
-        specified, a random module will be chosen from the default options.
-      **kwargs: additional arguments to pass to the superclass.
+      **kwargs: arguments to pass to the base class.
     """
 
     super().__init__(
-        model=model,
-        embedder=embedder,
-        measurements=measurements,
-        agent_module=agent_module,
-        resident_visitor_modules=resident_visitor_modules,
-        supporting_agent_module=supporting_agent_module,
-        time_and_place_module=time_and_place_module,
         pub_closed_probability=0.7,
         **kwargs,
     )

examples/modular/environment/pub_coordination_closures_friendships.py

@@ -14,61 +14,26 @@
 
 """A Concordia Environment Configuration."""
 
-from collections.abc import Callable, Sequence
-import types
 
 from examples.modular.environment import pub_coordination
-from concordia.factory.agent import basic_agent
-from concordia.language_model import language_model
-from concordia.utils import measurements as measurements_lib
-import numpy as np
 
 
 class Simulation(pub_coordination.Simulation):
   """Simulation with pub closures."""
 
   def __init__(
       self,
-      model: language_model.LanguageModel,
-      embedder: Callable[[str], np.ndarray],
-      measurements: measurements_lib.Measurements,
-      agent_module: types.ModuleType = basic_agent,
-      resident_visitor_modules: Sequence[types.ModuleType] | None = None,
-      supporting_agent_module: types.ModuleType | None = None,
-      time_and_place_module: str | None = None,
       **kwargs,
   ):
     """Initialize the simulation object.
 
     The launch script assumes this API object has a run() method.
 
     Args:
-      model: the language model to use.
-      embedder: the sentence transformer to use.
-      measurements: the measurements object to use.
-      agent_module: the agent module to use for all main characters.
-      resident_visitor_modules: optionally, use different modules for majority
-        and minority parts of the focal population.
-      supporting_agent_module: agent module to use for all supporting players. A
-        supporting player is a non-player character with a persistent memory
-        that plays a specific role in defining the task environment. Their role
-        is not incidental but rather is critcal to making the task what it is.
-        Supporting players are not necessarily interchangeable with focal or
-        background players.
-      time_and_place_module: optionally, specify a module containing settings
-        that create a sense of setting in a specific time and place. If not
-        specified, a random module will be chosen from the default options.
-      **kwargs: additional arguments to pass to the superclass.
+      **kwargs: arguments to pass to the base class.
     """
 
     super().__init__(
-        model=model,
-        embedder=embedder,
-        measurements=measurements,
-        agent_module=agent_module,
-        resident_visitor_modules=resident_visitor_modules,
-        supporting_agent_module=supporting_agent_module,
-        time_and_place_module=time_and_place_module,
         pub_closed_probability=0.7,
         use_relational_matrix=True,
         **kwargs,

examples/modular/environment/pub_coordination_friendships.py

@@ -14,61 +14,22 @@
 
 """A Concordia Environment Configuration."""
 
-from collections.abc import Callable, Sequence
-import types
-
 from examples.modular.environment import pub_coordination
-from concordia.factory.agent import basic_agent
-from concordia.language_model import language_model
-from concordia.utils import measurements as measurements_lib
-import numpy as np
 
 
 class Simulation(pub_coordination.Simulation):
   """Simulation with pub closures."""
 
-  def __init__(
-      self,
-      model: language_model.LanguageModel,
-      embedder: Callable[[str], np.ndarray],
-      measurements: measurements_lib.Measurements,
-      agent_module: types.ModuleType = basic_agent,
-      resident_visitor_modules: Sequence[types.ModuleType] | None = None,
-      supporting_agent_module: types.ModuleType | None = None,
-      time_and_place_module: str | None = None,
-      **kwargs,
-  ):
+  def __init__(self, **kwargs):
     """Initialize the simulation object.
 
     The launch script assumes this API object has a run() method.
 
     Args:
-      model: the language model to use.
-      embedder: the sentence transformer to use.
-      measurements: the measurements object to use.
-      agent_module: the agent module to use for all main characters.
-      resident_visitor_modules: optionally, use different modules for majority
-        and minority parts of the focal population.
-      supporting_agent_module: agent module to use for all supporting players. A
-        supporting player is a non-player character with a persistent memory
-        that plays a specific role in defining the task environment. Their role
-        is not incidental but rather is critcal to making the task what it is.
-        Supporting players are not necessarily interchangeable with focal or
-        background players.
-      time_and_place_module: optionally, specify a module containing settings
-        that create a sense of setting in a specific time and place. If not
-        specified, a random module will be chosen from the default options.
-      **kwargs: additional arguments to pass to the superclass.
+      **kwargs: arguments to pass to the base class.
     """
 
     super().__init__(
-        model=model,
-        embedder=embedder,
-        measurements=measurements,
-        agent_module=agent_module,
-        resident_visitor_modules=resident_visitor_modules,
-        supporting_agent_module=supporting_agent_module,
-        time_and_place_module=time_and_place_module,
         use_relational_matrix=True,
         **kwargs,
     )

examples/modular/environment/pub_coordination_mini.py

@@ -14,61 +14,26 @@
 
 """A Concordia Environment Configuration."""
 
-from collections.abc import Callable, Sequence
-import types
 
 from examples.modular.environment import pub_coordination
-from concordia.factory.agent import basic_agent
-from concordia.language_model import language_model
-from concordia.utils import measurements as measurements_lib
-import numpy as np
 
 
 class Simulation(pub_coordination.Simulation):
   """Simulation with pub closures."""
 
   def __init__(
       self,
-      model: language_model.LanguageModel,
-      embedder: Callable[[str], np.ndarray],
-      measurements: measurements_lib.Measurements,
-      agent_module: types.ModuleType = basic_agent,
-      resident_visitor_modules: Sequence[types.ModuleType] | None = None,
-      supporting_agent_module: types.ModuleType | None = None,
-      time_and_place_module: str | None = None,
       **kwargs,
   ):
     """Initialize the simulation object.
 
     The launch script assumes this API object has a run() method.
 
     Args:
-      model: the language model to use.
-      embedder: the sentence transformer to use.
-      measurements: the measurements object to use.
-      agent_module: the agent module to use for all main characters.
-      resident_visitor_modules: optionally, use different modules for majority
-        and minority parts of the focal population.
-      supporting_agent_module: agent module to use for all supporting players. A
-        supporting player is a non-player character with a persistent memory
-        that plays a specific role in defining the task environment. Their role
-        is not incidental but rather is critcal to making the task what it is.
-        Supporting players are not necessarily interchangeable with focal or
-        background players.
-      time_and_place_module: optionally, specify a module containing settings
-        that create a sense of setting in a specific time and place. If not
-        specified, a random module will be chosen from the default options.
-      **kwargs: additional arguments to pass to the superclass.
+      **kwargs: arguments to pass to the base class.
     """
 
     super().__init__(
-        model=model,
-        embedder=embedder,
-        measurements=measurements,
-        agent_module=agent_module,
-        resident_visitor_modules=resident_visitor_modules,
-        supporting_agent_module=supporting_agent_module,
-        time_and_place_module=time_and_place_module,
         pub_closed_probability=0.0,
         num_games=1,
         num_main_players=2,