#2869 - Changes following review discussion

src/primaite/game/agent/interface.py

@@ -49,13 +49,12 @@ class AbstractAgent(BaseModel):
     _registry: ClassVar[Dict[str, Type[AbstractAgent]]] = {}
     _logger: AgentLog = AgentLog(agent_name="Abstract_Agent")
 
-    config: "AbstractAgent.ConfigSchema"
     history: List[AgentHistoryItem] = []
+    config: "AbstractAgent.ConfigSchema"
     action_manager: ActionManager
     observation_manager: ObservationManager
     reward_function: RewardFunction
 
-
     class ConfigSchema(BaseModel):
         """
         Configuration Schema for AbstractAgents.
@@ -85,14 +84,14 @@ class AbstractAgent(BaseModel):
         variance: int = 0
         "The amount the frequency can randomly change to"
 
-
         @model_validator(mode="after")
         def check_variance_lt_frequency(self) -> "AbstractAgent.ConfigSchema":
             """
             Make sure variance is equal to or lower than frequency.
 
-            This is because the calculation for the next execution time is now + (frequency +- variance). If variance were
+            This is because the calculation for the next execution time is now + (frequency +- variance).
-            greater than frequency, sometimes the bracketed term would be negative and the attack would never happen again.
+            If variance were greater than frequency, sometimes the bracketed term would be negative
+            and the attack would never happen again.
             """
             if self.variance > self.frequency:
                 raise ValueError(
@@ -101,14 +100,12 @@ class AbstractAgent(BaseModel):
                 )
             return self
 
-
     def __init_subclass__(cls, identifier: str, **kwargs: Any) -> None:
         if identifier in cls._registry:
             raise ValueError(f"Cannot create a new agent under reserved name {identifier}")
         cls._registry[identifier] = cls
         super().__init_subclass__(**kwargs)
 
-
     @property
     def flatten_obs(self) -> bool:
         """Return agent flatten_obs param."""
@@ -117,7 +114,12 @@ class AbstractAgent(BaseModel):
     @classmethod
     def from_config(cls, config: Dict) -> "AbstractAgent":
         """Creates an agent component from a configuration dictionary."""
-        obj = cls(config=cls.ConfigSchema(**config))
+        obj = cls(
+            config=cls.ConfigSchema(**config["agent_settings"]),
+            action_manager=ActionManager.from_config(**config["action_manager"]),
+            observation_manager=ObservationManager.from_config(**config["observation_space"]),
+            reward_function=RewardFunction.from_config(**config["reward_function"]),
+        )
         return obj
 
     def update_observation(self, state: Dict) -> ObsType:
@@ -206,9 +208,8 @@ class ProxyAgent(AbstractAgent, identifier="ProxyAgent"):
         """Configuration Schema for Proxy Agent."""
 
         agent_name: str = "Proxy_Agent"
-        agent_settings: AgentSettings = None
+        flatten_obs: bool = False
-        flatten_obs: bool = agent_settings.flatten_obs if agent_settings else False
+        action_masking: bool = False
-        action_masking: bool = agent_settings.action_masking if agent_settings else False
 
     def get_action(self, obs: ObsType, timestep: int = 0) -> Tuple[str, Dict]:
         """
@@ -221,7 +222,7 @@ class ProxyAgent(AbstractAgent, identifier="ProxyAgent"):
         :return: Action to be taken in CAOS format.
         :rtype: Tuple[str, Dict]
         """
-        return self.config.action_manager.get_action(self.most_recent_action)
+        return self.action_manager.get_action(self.most_recent_action)
 
     def store_action(self, action: ActType):
         """

src/primaite/game/game.py

@@ -7,10 +7,8 @@ import numpy as np
 from pydantic import BaseModel, ConfigDict
 
 from primaite import DEFAULT_BANDWIDTH, getLogger
-from primaite.game.agent.actions import ActionManager
-from primaite.game.agent.observations.observation_manager import ObservationManager
-from primaite.game.agent.rewards import RewardFunction, SharedReward
 from primaite.game.agent.interface import AbstractAgent, ProxyAgent
+from primaite.game.agent.rewards import SharedReward
 from primaite.game.science import graph_has_cycle, topological_sort
 from primaite.simulator import SIM_OUTPUT
 from primaite.simulator.network.creation import NetworkNodeAdder
@@ -532,24 +530,14 @@ class PrimaiteGame:
             action_space_cfg = agent_cfg["action_space"]
             observation_space_cfg = agent_cfg["observation_space"]
             reward_function_cfg = agent_cfg["reward_function"]
-
+            agent_settings = agent_cfg["agent_settings"]
-            # CREATE OBSERVATION SPACE
-            obs_space = ObservationManager.from_config(observation_space_cfg)
-
-            # CREATE ACTION SPACE
-            action_space = ActionManager.from_config(game, action_space_cfg)
-
-            # CREATE REWARD FUNCTION
-            reward_function = RewardFunction.from_config(reward_function_cfg)
 
             # CREATE AGENT
-
-            agent_settings = agent_cfg["agent_settings"]
             agent_config = {
                 "agent_name": agent_ref,
-                "action_manager": action_space,
+                "action_manager": action_space_cfg,
-                "observation_manager": obs_space,
+                "observation_manager": observation_space_cfg,
-                "reward_function": reward_function,
+                "reward_function": reward_function_cfg,
                 "agent_settings": agent_settings,
             }