Add factory for a rational supporting character.

PiperOrigin-RevId: 657516116
Change-Id: Ie5dd60f898ab71e8e68763fbafcc61c613ef4c89

concordia/factory/agent/rational_entity_agent__supporting_role.py

@@ -0,0 +1,196 @@
+# Copyright 2024 DeepMind Technologies Limited.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""A Generic Agent Factory."""
+
+from collections.abc import Mapping
+import datetime
+import types
+
+from concordia.agents import basic_agent
+from concordia.agents import entity_agent_with_logging
+from concordia.associative_memory import associative_memory
+from concordia.associative_memory import formative_memories
+from concordia.clocks import game_clock
+from concordia.components.agent import v2 as agent_components
+from concordia.language_model import language_model
+from concordia.memory_bank import legacy_associative_memory
+from concordia.typing import entity_component
+from concordia.utils import measurements as measurements_lib
+
+
+def _get_class_name(object_: object) -> str:
+  return object_.__class__.__name__
+
+
+def build_agent(
+    config: formative_memories.AgentConfig,
+    model: language_model.LanguageModel,
+    memory: associative_memory.AssociativeMemory,
+    clock: game_clock.MultiIntervalClock,
+    update_time_interval: datetime.timedelta,
+    additional_components: Mapping[
+        entity_component.ComponentName, str
+    ] = types.MappingProxyType({}),
+) -> basic_agent.BasicAgent:
+  """Build an agent.
+
+  Args:
+    config: The agent config to use.
+    model: The language model to use.
+    memory: The agent's memory object.
+    clock: The clock to use.
+    update_time_interval: Agent calls update every time this interval passes.
+    additional_components: Additional components to add to the agent.
+
+  Returns:
+    An agent.
+  """
+  del update_time_interval
+  if config.extras.get('main_character', False):
+    raise ValueError('This function is meant for a supporting character '
+                     'but it was called on a main character.')
+
+  agent_name = config.name
+  raw_memory = legacy_associative_memory.AssociativeMemoryBank(memory)
+  measurements = measurements_lib.Measurements()
+
+  instructions = agent_components.instructions.Instructions(
+      agent_name=agent_name,
+      logging_channel=measurements.get_channel('Instructions').on_next,
+  )
+  time_display = agent_components.report_function.ReportFunction(
+      function=clock.current_time_interval_str,
+      pre_act_key='\nCurrent time',
+      logging_channel=measurements.get_channel('TimeDisplay').on_next,
+  )
+  observation_label = '\nObservation'
+  observation = agent_components.observation.Observation(
+      clock_now=clock.now,
+      timeframe=clock.get_step_size(),
+      pre_act_key=observation_label,
+      logging_channel=measurements.get_channel('Observation').on_next,
+  )
+  observation_summary_label = '\nSummary of recent observations'
+  observation_summary = agent_components.observation.ObservationSummary(
+      model=model,
+      clock_now=clock.now,
+      timeframe_delta_from=datetime.timedelta(hours=4),
+      timeframe_delta_until=datetime.timedelta(hours=0),
+      pre_act_key=observation_summary_label,
+      logging_channel=measurements.get_channel('ObservationSummary').on_next,
+  )
+  relevant_memories_label = '\nRecalled memories and observations'
+  relevant_memories = agent_components.all_similar_memories.AllSimilarMemories(
+      model=model,
+      components={
+          _get_class_name(observation_summary): observation_summary_label,
+          _get_class_name(time_display): 'The current date/time is'},
+      num_memories_to_retrieve=10,
+      pre_act_key=relevant_memories_label,
+      logging_channel=measurements.get_channel('AllSimilarMemories').on_next,
+  )
+
+  options_perception_components = {}
+  if config.goal:
+    goal_label = '\nOverarching goal'
+    overarching_goal = agent_components.constant.Constant(
+        state=config.goal,
+        pre_act_key=goal_label,
+        logging_channel=measurements.get_channel(goal_label).on_next)
+    options_perception_components[goal_label] = goal_label
+  else:
+    goal_label = None
+    overarching_goal = None
+
+  options_perception_components.update({
+      _get_class_name(observation): observation_label,
+      _get_class_name(observation_summary): observation_summary_label,
+      _get_class_name(relevant_memories): relevant_memories_label,
+  })
+  options_perception_label = (
+      f'\nQuestion: Which options are available to {agent_name} '
+      'right now?\nAnswer')
+  options_perception = (
+      agent_components.options_perception.AvailableOptionsPerception(
+          model=model,
+          components=options_perception_components,
+          clock_now=clock.now,
+          pre_act_key=options_perception_label,
+          logging_channel=measurements.get_channel(
+              'AvailableOptionsPerception').on_next,
+      )
+  )
+  best_option_perception_label = (
+      f'\nQuestion: Of the options available to {agent_name}, and '
+      'given their goal, which choice of action or strategy is '
+      f'best for {agent_name} to take right now?\nAnswer')
+  best_option_perception = {}
+  if config.goal:
+    best_option_perception[goal_label] = goal_label
+  best_option_perception.update({
+      _get_class_name(observation): observation_label,
+      _get_class_name(observation_summary): observation_summary_label,
+      _get_class_name(relevant_memories): relevant_memories_label,
+      _get_class_name(options_perception): options_perception_label,
+  })
+  best_option_perception = (
+      agent_components.options_perception.BestOptionPerception(
+          model=model,
+          components=best_option_perception,
+          clock_now=clock.now,
+          pre_act_key=best_option_perception_label,
+          logging_channel=measurements.get_channel(
+              'BestOptionPerception').on_next,
+      )
+  )
+
+  entity_components = (
+      # Components that provide pre_act context.
+      instructions,
+      time_display,
+      observation,
+      observation_summary,
+      relevant_memories,
+      options_perception,
+      best_option_perception,
+  )
+
+  components_of_agent = {_get_class_name(component): component
+                         for component in entity_components}
+  components_of_agent[
+      agent_components.memory_component.DEFAULT_MEMORY_COMPONENT_NAME] = (
+          agent_components.memory_component.MemoryComponent(raw_memory))
+  components_of_agent.update(additional_components)
+
+  component_order = list(components_of_agent.keys())
+  if overarching_goal is not None:
+    components_of_agent[goal_label] = overarching_goal
+    # Place goal after the instructions.
+    component_order.insert(1, goal_label)
+
+  act_component = agent_components.concat_act_component.ConcatActComponent(
+      model=model,
+      clock=clock,
+      logging_channel=measurements.get_channel('ActComponent').on_next,
+  )
+
+  agent = entity_agent_with_logging.EntityAgentWithLogging(
+      agent_name=agent_name,
+      act_component=act_component,
+      context_components=components_of_agent,
+      component_logging=measurements,
+  )
+
+  return agent