Pass agent settings from config to agent

src/primaite/config/_package_data/example_config.yaml

@@ -50,6 +50,7 @@ game_config:
           - type: DUMMY
 
       agent_settings:
+        start_settings:
           start_step: 5
           frequency: 4
           variance: 3
@@ -106,6 +107,7 @@ game_config:
           - type: DUMMY
 
       agent_settings: # options specific to this particular agent type, basically args of __init__(self)
+        start_settings:
           start_step: 25
           frequency: 20
           variance: 5

src/primaite/game/agent/interface.py

@@ -25,6 +25,24 @@ class AgentExecutionDefinition(BaseModel):
     "The probability of data manipulation succeeding."
 
 
+class AgentStartSettings(BaseModel):
+    """Configuration values for when an agent starts performing actions."""
+
+    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"
+
+
+class AgentSettings(BaseModel):
+    """Settings for configuring the operation of an agent."""
+
+    start_settings: Optional[AgentStartSettings] = None
+    "Configuration for when an agent begins performing it's actions"
+
+
 class AbstractAgent(ABC):
     """Base class for scripted and RL agents."""
 
@@ -35,6 +53,7 @@ class AbstractAgent(ABC):
         observation_space: Optional[ObservationSpace],
         reward_function: Optional[RewardFunction],
         execution_definition: Optional[AgentExecutionDefinition],
+        agent_settings: Optional[AgentSettings],
     ) -> None:
         """
         Initialize an agent.
@@ -57,6 +76,8 @@ class AbstractAgent(ABC):
         # by for example specifying target ip addresses, or converting a node ID into a uuid
         self.execution_definition = execution_definition or AgentExecutionDefinition()
 
+        self.agent_settings = agent_settings or AgentSettings()
+
     def convert_state_to_obs(self, state: Dict) -> ObsType:
         """
         Convert a state from the simulator into an observation for the agent using the observation space.

src/primaite/game/session.py

@@ -10,7 +10,13 @@ from pydantic import BaseModel
 
 from primaite import getLogger
 from primaite.game.agent.actions import ActionManager
-from primaite.game.agent.interface import AbstractAgent, AgentExecutionDefinition, DataManipulationAgent, RandomAgent
+from primaite.game.agent.interface import (
+    AbstractAgent,
+    AgentExecutionDefinition,
+    AgentSettings,
+    DataManipulationAgent,
+    RandomAgent,
+)
 from primaite.game.agent.observations import ObservationSpace
 from primaite.game.agent.rewards import RewardFunction
 from primaite.simulator.network.hardware.base import Link, NIC, Node
@@ -439,6 +445,7 @@ class PrimaiteSession:
             rew_function = RewardFunction.from_config(reward_function_cfg, session=sess)
 
             execution_definition = AgentExecutionDefinition(**agent_cfg.get("execution_definition", {}))
+            agent_settings = AgentSettings(**agent_cfg.get("agent_settings", {}))
 
             # CREATE AGENT
             if agent_type == "GreenWebBrowsingAgent":
@@ -449,6 +456,7 @@ class PrimaiteSession:
                     observation_space=obs_space,
                     reward_function=rew_function,
                     execution_definition=execution_definition,
+                    agent_settings=agent_settings,
                 )
                 sess.agents.append(new_agent)
             elif agent_type == "GATERLAgent":
@@ -458,6 +466,7 @@ class PrimaiteSession:
                     observation_space=obs_space,
                     reward_function=rew_function,
                     execution_definition=execution_definition,
+                    agent_settings=agent_settings,
                 )
                 sess.agents.append(new_agent)
                 sess.rl_agent = new_agent
@@ -468,6 +477,7 @@ class PrimaiteSession:
                     observation_space=obs_space,
                     reward_function=rew_function,
                     execution_definition=execution_definition,
+                    agent_settings=agent_settings,
                 )
                 sess.agents.append(new_agent)
             else: