#2869 - Update Config for some agent classes to use pydantic.Field, amend some identifiers and agent_name variables

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, model_validator
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 
 from primaite.game.agent.actions import ActionManager
 from primaite.game.agent.agent_log import AgentLog
@@ -50,7 +50,7 @@ class AbstractAgent(BaseModel):
     logger: AgentLog = AgentLog(agent_name="Abstract_Agent")
 
     history: List[AgentHistoryItem] = []
-    config: "AbstractAgent.ConfigSchema"
+    config: "AbstractAgent.ConfigSchema" = Field(default_factory=lambda: AbstractAgent.ConfigSchema())
     action_manager: "ActionManager"
     observation_manager: "ObservationManager"
     reward_function: "RewardFunction"
@@ -62,8 +62,6 @@ class AbstractAgent(BaseModel):
 
         :param type: Type of agent being generated.
         :type type: str
-        :param agent_name: Unique string identifier for the agent, for reporting and multi-agent purposes.
-        :type agent_name: str
         :param observation_space: Observation space for the agent.
         :type observation_space: Optional[ObservationSpace]
         :param reward_function: Reward function for the agent.
@@ -73,7 +71,7 @@ class AbstractAgent(BaseModel):
         """
 
         model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)
-        agent_name: str = "Abstract_Agent"
+        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
@@ -185,15 +183,15 @@ class AbstractAgent(BaseModel):
         self.history[-1].reward = self.reward_function.current_reward
 
 
-class AbstractScriptedAgent(AbstractAgent, identifier="Abstract_Scripted_Agent"):
+class AbstractScriptedAgent(AbstractAgent, identifier="AbstractScriptedAgent"):
     """Base class for actors which generate their own behaviour."""
 
-    config: "AbstractScriptedAgent.ConfigSchema"
+    config: "AbstractScriptedAgent.ConfigSchema" = Field(default_factory=lambda: AbstractScriptedAgent.ConfigSchema())
 
     class ConfigSchema(AbstractAgent.ConfigSchema):
         """Configuration Schema for AbstractScriptedAgents."""
 
-        agent_name: str = "Abstract_Scripted_Agent"
+        type: str = "AbstractScriptedAgent"
 
     @abstractmethod
     def get_action(self, obs: ObsType, timestep: int = 0) -> Tuple[str, Dict]:
@@ -204,13 +202,13 @@ class AbstractScriptedAgent(AbstractAgent, identifier="Abstract_Scripted_Agent")
 class ProxyAgent(AbstractAgent, identifier="ProxyAgent"):
     """Agent that sends observations to an RL model and receives actions from that model."""
 
-    config: "ProxyAgent.ConfigSchema"
+    config: "ProxyAgent.ConfigSchema" = Field(default_factory=lambda: ProxyAgent.ConfigSchema())
     most_recent_action: ActType = None
 
     class ConfigSchema(AbstractAgent.ConfigSchema):
         """Configuration Schema for Proxy Agent."""
 
-        agent_name: str = "Proxy_Agent"
+        type: str = "Proxy_Agent"
         flatten_obs: bool = False
         action_masking: bool = False
 

src/primaite/game/game.py

@@ -533,7 +533,7 @@ class PrimaiteGame:
             agent_settings = agent_cfg["agent_settings"]
 
             agent_config = {
-                "agent_name": agent_name,
+                "type": agent_type,
                 "action_manager": action_space_cfg,
                 "observation_manager": observation_space_cfg,
                 "reward_function": reward_function_cfg,

tests/conftest.py

@@ -3,6 +3,7 @@ from typing import Any, Dict, Optional, Tuple
 
 import pytest
 import yaml
+from pydantic import Field
 from ray import init as rayinit
 
 from primaite import getLogger, PRIMAITE_PATHS
@@ -265,16 +266,16 @@ def example_network() -> Network:
     return network
 
 
-class ControlledAgent(AbstractAgent, identifier="Controlled_Agent"):
+class ControlledAgent(AbstractAgent, identifier="ControlledAgent"):
     """Agent that can be controlled by the tests."""
 
-    config: "ControlledAgent.ConfigSchema"
+    config: "ControlledAgent.ConfigSchema" = Field(default_factory=lambda: ControlledAgent.ConfigSchema())
     most_recent_action: Optional[Tuple[str, Dict]] = None
 
     class ConfigSchema(AbstractAgent.ConfigSchema):
         """Configuration Schema for Abstract Agent used in tests."""
 
-        agent_name: str = "Controlled_Agent"
+        type: str = "ControlledAgent"
 
     def get_action(self, obs: None, timestep: int = 0) -> Tuple[str, Dict]:
         """Return the agent's most recent action, formatted in CAOS format."""