#2869 - Update type hints and ConfigSchema variables in some agent classes

src/primaite/game/agent/interface.py

@@ -6,7 +6,7 @@ from abc import abstractmethod
 from typing import Any, ClassVar, Dict, List, Optional, Tuple, Type, TYPE_CHECKING
 
 from gymnasium.core import ActType, ObsType
-from pydantic import BaseModel, ConfigDict, Field, model_validator
+from pydantic import BaseModel, ConfigDict, Field
 
 from primaite.game.agent.actions import ActionManager
 from primaite.game.agent.agent_log import AgentLog
@@ -72,32 +72,6 @@ class AbstractAgent(BaseModel):
 
         model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)
         type: str = "AbstractAgent"
-        flatten_obs: bool = True
-        "Whether to flatten the observation space before passing it to the agent. True by default."
-        action_masking: bool = False
-        "Whether to return action masks at each step."
-        start_step: int = 5
-        "The timestep at which an agent begins performing it's actions"
-        frequency: int = 5
-        "The number of timesteps to wait between performing actions"
-        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 greater than frequency, sometimes the bracketed term would be negative
-            and the attack would never happen again.
-            """
-            if self.variance > self.frequency:
-                raise ValueError(
-                    f"Agent start settings error: variance must be lower than frequency "
-                    f"{self.variance=}, {self.frequency=}"
-                )
-            return self
 
     def __init_subclass__(cls, identifier: str, **kwargs: Any) -> None:
         if identifier in cls._registry:

src/primaite/game/agent/scripted_agents/probabilistic_agent.py

@@ -1,10 +1,11 @@
 # © Crown-owned copyright 2025, Defence Science and Technology Laboratory UK
 """Agents with predefined behaviours."""
-from typing import Any, Dict, Tuple
+from typing import Dict, Tuple
 
 import numpy as np
 import pydantic
 from gymnasium.core import ObsType
+from numpy.random import Generator
 from pydantic import Field
 
 from primaite.game.agent.interface import AbstractScriptedAgent
@@ -16,7 +17,7 @@ class ProbabilisticAgent(AbstractScriptedAgent, identifier="ProbabilisticAgent")
     """Scripted agent which randomly samples its action space with prescribed probabilities for each action."""
 
     config: "ProbabilisticAgent.ConfigSchema" = Field(default_factory=lambda: ProbabilisticAgent.ConfigSchema())
-    rng: Any = np.random.default_rng(np.random.randint(0, 65535))
+    rng: Generator = np.random.default_rng(np.random.randint(0, 65535))
 
     class ConfigSchema(AbstractScriptedAgent.ConfigSchema):
         """Configuration schema for Probabilistic Agent."""

src/primaite/game/agent/scripted_agents/random_agent.py

@@ -3,7 +3,7 @@ import random
 from typing import Dict, Tuple
 
 from gymnasium.core import ObsType
-from pydantic import Field
+from pydantic import Field, model_validator
 
 from primaite.game.agent.interface import AbstractScriptedAgent
 
@@ -43,6 +43,28 @@ class PeriodicAgent(AbstractScriptedAgent, identifier="PeriodicAgent"):
 
         type: str = "PeriodicAgent"
         """Name of the agent."""
+        start_step: int = 5
+        "The timestep at which an agent begins performing it's actions"
+        frequency: int = 5
+        "The number of timesteps to wait between performing actions"
+        variance: int = 0
+        "The amount the frequency can randomly change to"
+
+        @model_validator(mode="after")
+        def check_variance_lt_frequency(self) -> "PeriodicAgent.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 greater than frequency, sometimes the bracketed term would be negative
+            and the attack would never happen again.
+            """
+            if self.variance > self.frequency:
+                raise ValueError(
+                    f"Agent start settings error: variance must be lower than frequency "
+                    f"{self.variance=}, {self.frequency=}"
+                )
+            return self
 
     max_executions: int = 999999
     "Maximum number of times the agent can execute its action."

tests/unit_tests/_primaite/_game/_agent/test_probabilistic_agent.py

@@ -3,7 +3,7 @@ from primaite.game.agent.actions import ActionManager
 from primaite.game.agent.observations.observation_manager import NestedObservation, ObservationManager
 from primaite.game.agent.rewards import RewardFunction
 from primaite.game.agent.scripted_agents.probabilistic_agent import ProbabilisticAgent
-from primaite.game.game import PrimaiteGame
+from primaite.game.game import PrimaiteGame, PrimaiteGameOptions
 
 
 def test_probabilistic_agent():
@@ -54,16 +54,17 @@ def test_probabilistic_agent():
         },
         "options": {},
     }
-    observation_space = ObservationManager(NestedObservation(components={}))
-    reward_function = RewardFunction()
+
+    game = PrimaiteGame()
+    game.options = PrimaiteGameOptions(ports=[], protocols=[])
 
     observation_space_cfg = None
 
     reward_function_cfg = {}
 
     pa_config = {
-        "agent_name": "test_agent",
-        "game": PrimaiteGame(),
+        "type": "ProbabilisticAgent",
+        "game": game,
         "action_manager": action_space_cfg,
         "observation_manager": observation_space_cfg,
         "reward_function": reward_function_cfg,