226 lines
8.7 KiB
Python
226 lines
8.7 KiB
Python
"""Interface for agents."""
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING
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from gymnasium.core import ActType, ObsType
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from pydantic import BaseModel, model_validator
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from primaite.game.agent.actions import ActionManager
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from primaite.game.agent.observations.observation_manager import ObservationManager
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from primaite.game.agent.rewards import RewardFunction
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from primaite.interface.request import RequestFormat, RequestResponse
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if TYPE_CHECKING:
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pass
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class AgentHistoryItem(BaseModel):
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"""One entry of an agent's action log - what the agent did and how the simulator responded in 1 step."""
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timestep: int
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"""Timestep of this action."""
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action: str
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"""CAOS Action name."""
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parameters: Dict[str, Any]
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"""CAOS parameters for the given action."""
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request: RequestFormat
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"""The request that was sent to the simulation based on the CAOS action chosen."""
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response: RequestResponse
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"""The response sent back by the simulator for this action."""
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reward: Optional[float] = None
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class AgentStartSettings(BaseModel):
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"""Configuration values for when an agent starts performing actions."""
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start_step: int = 5
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"The timestep at which an agent begins performing it's actions"
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frequency: int = 5
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"The number of timesteps to wait between performing actions"
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variance: int = 0
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"The amount the frequency can randomly change to"
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@model_validator(mode="after")
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def check_variance_lt_frequency(self) -> "AgentStartSettings":
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"""
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Make sure variance is equal to or lower than frequency.
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This is because the calculation for the next execution time is now + (frequency +- variance). If variance were
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greater than frequency, sometimes the bracketed term would be negative and the attack would never happen again.
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"""
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if self.variance > self.frequency:
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raise ValueError(
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f"Agent start settings error: variance must be lower than frequency "
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f"{self.variance=}, {self.frequency=}"
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)
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return self
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class AgentSettings(BaseModel):
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"""Settings for configuring the operation of an agent."""
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start_settings: Optional[AgentStartSettings] = None
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"Configuration for when an agent begins performing it's actions"
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flatten_obs: bool = True
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"Whether to flatten the observation space before passing it to the agent. True by default."
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@classmethod
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def from_config(cls, config: Optional[Dict]) -> "AgentSettings":
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"""Construct agent settings from a config dictionary.
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:param config: A dict of options for the agent settings.
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:type config: Dict
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:return: The agent settings.
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:rtype: AgentSettings
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"""
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if config is None:
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return cls()
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return cls(**config)
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class AbstractAgent(ABC):
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"""Base class for scripted and RL agents."""
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def __init__(
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self,
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agent_name: Optional[str],
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action_space: Optional[ActionManager],
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observation_space: Optional[ObservationManager],
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reward_function: Optional[RewardFunction],
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agent_settings: Optional[AgentSettings] = None,
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) -> None:
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"""
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Initialize an agent.
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:param agent_name: Unique string identifier for the agent, for reporting and multi-agent purposes.
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:type agent_name: Optional[str]
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:param action_space: Action space for the agent.
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:type action_space: Optional[ActionManager]
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:param observation_space: Observation space for the agent.
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:type observation_space: Optional[ObservationSpace]
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:param reward_function: Reward function for the agent.
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:type reward_function: Optional[RewardFunction]
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"""
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self.agent_name: str = agent_name or "unnamed_agent"
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self.action_manager: Optional[ActionManager] = action_space
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self.observation_manager: Optional[ObservationManager] = observation_space
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self.reward_function: Optional[RewardFunction] = reward_function
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self.agent_settings = agent_settings or AgentSettings()
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self.history: List[AgentHistoryItem] = []
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def update_observation(self, state: Dict) -> ObsType:
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"""
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Convert a state from the simulator into an observation for the agent using the observation space.
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state : dict state directly from simulation.describe_state
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output : dict state according to CAOS.
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"""
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return self.observation_manager.update(state)
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def update_reward(self, state: Dict) -> float:
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"""
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Use the reward function to calculate a reward from the state.
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:param state: State of the environment.
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:type state: Dict
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:return: Reward from the state.
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:rtype: float
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"""
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return self.reward_function.update(state=state, last_action_response=self.history[-1])
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@abstractmethod
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def get_action(self, obs: ObsType, timestep: int = 0) -> Tuple[str, Dict]:
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"""
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Return an action to be taken in the environment.
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Subclasses should implement agent logic here. It should use the observation as input to decide best next action.
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:param obs: Observation of the environment.
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:type obs: ObsType
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:param timestep: The current timestep in the simulation, used for non-RL agents. Optional
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:type timestep: int
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:return: Action to be taken in the environment.
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:rtype: Tuple[str, Dict]
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"""
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# in RL agent, this method will send CAOS observation to RL agent, then receive a int 0-39,
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# then use a bespoke conversion to take 1-40 int back into CAOS action
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return ("DO_NOTHING", {})
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def format_request(self, action: Tuple[str, Dict], options: Dict[str, int]) -> List[str]:
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# this will take something like APPLICATION.EXECUTE and add things like target_ip_address in simulator.
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# therefore the execution definition needs to be a mapping from CAOS into SIMULATOR
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"""Format action into format expected by the simulator, and apply execution definition if applicable."""
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request = self.action_manager.form_request(action_identifier=action, action_options=options)
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return request
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def process_action_response(
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self, timestep: int, action: str, parameters: Dict[str, Any], request: RequestFormat, response: RequestResponse
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) -> None:
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"""Process the response from the most recent action."""
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self.history.append(
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AgentHistoryItem(
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timestep=timestep, action=action, parameters=parameters, request=request, response=response
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)
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)
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def save_reward_to_history(self) -> None:
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"""Update the most recent history item with the reward value."""
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self.history[-1].reward = self.reward_function.current_reward
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class AbstractScriptedAgent(AbstractAgent):
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"""Base class for actors which generate their own behaviour."""
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@abstractmethod
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def get_action(self, obs: ObsType, timestep: int = 0) -> Tuple[str, Dict]:
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"""Return an action to be taken in the environment."""
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return super().get_action(obs=obs, timestep=timestep)
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class ProxyAgent(AbstractAgent):
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"""Agent that sends observations to an RL model and receives actions from that model."""
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def __init__(
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self,
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agent_name: Optional[str],
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action_space: Optional[ActionManager],
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observation_space: Optional[ObservationManager],
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reward_function: Optional[RewardFunction],
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agent_settings: Optional[AgentSettings] = None,
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) -> None:
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super().__init__(
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agent_name=agent_name,
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action_space=action_space,
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observation_space=observation_space,
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reward_function=reward_function,
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)
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self.most_recent_action: ActType
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self.flatten_obs: bool = agent_settings.flatten_obs if agent_settings else False
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def get_action(self, obs: ObsType, timestep: int = 0) -> Tuple[str, Dict]:
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"""
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Return the agent's most recent action, formatted in CAOS format.
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:param obs: Observation for the agent. Not used by ProxyAgents, but required by the interface.
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:type obs: ObsType
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:param timestep: Current simulation timestep. Not used by ProxyAgents, bur required for the interface.
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:type timestep: int
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:return: Action to be taken in CAOS format.
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:rtype: Tuple[str, Dict]
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"""
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return self.action_manager.get_action(self.most_recent_action)
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def store_action(self, action: ActType):
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"""
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Store the most recent action taken by the agent.
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The environment is responsible for calling this method when it receives an action from the agent policy.
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"""
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self.most_recent_action = action
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