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#2869 - Updates to Probabilistic Agent to follow the defined extensibility schema.

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This commit is contained in:
Charlie Crane
2024-12-11 11:58:42 +00:00
parent 1798ec6fe0
commit feee028485
5 changed files with 12 additions and 149 deletions
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1
src/primaite/game/agent/agent_log.py
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@@ -1,6 +1,5 @@
# © Crown-owned copyright 2024, Defence Science and Technology Laboratory UK
import logging
from abc import ABC
from pathlib import Path
from typing import Optional

43
src/primaite/game/agent/scripted_agents/abstract_tap.py
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@@ -1,43 +0,0 @@
# © Crown-owned copyright 2024, Defence Science and Technology Laboratory UK
from __future__ import annotations
import random
from abc import abstractmethod
from primaite.game.agent.interface import AbstractScriptedAgent
class AbstractTAPAgent(AbstractScriptedAgent, identifier="Abstract_TAP"):
"""Base class for TAP agents to inherit from."""
config: "AbstractTAPAgent.ConfigSchema"
class ConfigSchema(AbstractScriptedAgent.ConfigSchema):
"""Configuration schema for Abstract TAP agents."""
agent_name: str = "Abstract_TAP"
starting_node_name: str
next_execution_timestep: int
@abstractmethod
def setup_agent(self) -> None:
"""Set up agent."""
pass
def _set_next_execution_timestep(self, timestep: int) -> None:
"""Set the next execution timestep with a configured random variance.
:param timestep: The timestep to add variance to.
"""
random_timestep_increment = random.randint(
-self.config.agent_settings.start_settings.variance, self.config.agent_settings.start_settings.variance
)
self.config.next_execution_timestep = timestep + random_timestep_increment
def _select_start_node(self) -> None:
"""Set the starting starting node of the agent to be a random node from this agent's action manager."""
# we are assuming that every node in the node manager has a data manipulation application at idx 0
num_nodes = len(self.config.action_manager.node_names)
starting_node_idx = random.randint(0, num_nodes - 1)
self.starting_node_name = self.config.action_manager.node_names[starting_node_idx]
self.config.logger.debug(f"Selected Starting node ID: {self.starting_node_name}")

1
src/primaite/game/agent/scripted_agents/data_manipulation_bot.py
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@@ -1,5 +1,4 @@
# © Crown-owned copyright 2024, Defence Science and Technology Laboratory UK
import random
from typing import Dict, Tuple
from gymnasium.core import ObsType

43
src/primaite/game/agent/scripted_agents/probabilistic_agent.py
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@@ -1,6 +1,6 @@
# © Crown-owned copyright 2024, Defence Science and Technology Laboratory UK
"""Agents with predefined behaviours."""
from typing import Dict, Optional, Tuple
from typing import Dict, Tuple
import numpy as np
import pydantic
@@ -8,23 +8,20 @@ from gymnasium.core import ObsType
from primaite.game.agent.actions import ActionManager
from primaite.game.agent.interface import AbstractScriptedAgent
from primaite.game.agent.observations.observation_manager import ObservationManager
from primaite.game.agent.rewards import RewardFunction
class ProbabilisticAgent(AbstractScriptedAgent, identifier="Probabilistic_Agent"):
"""Scripted agent which randomly samples its action space with prescribed probabilities for each action."""
class ConfigSchema(pydantic.BaseModel):
"""Config schema for Probabilistic agent settings."""
config: "ProbabilisticAgent.ConfigSchema"
class ConfigSchema(AbstractScriptedAgent.ConfigSchema):
"""Configuration schema for Probabilistic Agent."""
agent_name: str = "Probabilistic_Agent"
model_config = pydantic.ConfigDict(extra="forbid")
"""Strict validation."""
action_space: ActionManager
action_probabilities: Dict[int, float]
"""Probability to perform each action in the action map. The sum of probabilities should sum to 1."""
# TODO: give the option to still set a random seed, but have it vary each episode in a predictable way
# for example if the user sets seed 123, have it be 123 + episode_num, so that each ep it's the next seed.
@pydantic.field_validator("action_probabilities", mode="after")
@classmethod
@@ -45,32 +42,16 @@ class ProbabilisticAgent(AbstractScriptedAgent, identifier="Probabilistic_Agent"
)
return v
def __init__(
self,
agent_name: str,
action_space: Optional[ActionManager],
observation_space: Optional[ObservationManager],
reward_function: Optional[RewardFunction],
settings: Dict = {},
) -> None:
# If the action probabilities are not specified, create equal probabilities for all actions
if "action_probabilities" not in settings:
num_actions = len(action_space.action_map)
settings = {"action_probabilities": {i: 1 / num_actions for i in range(num_actions)}}
# The random number seed for np.random is dependent on whether a random number seed is set
# in the config file. If there is one it is processed by set_random_seed() in environment.py
# and as a consequence the the sequence of rng_seed's used here will be repeatable.
self.settings = ProbabilisticAgent.ConfigSchema(**settings)
def __init__(self) -> None:
rng_seed = np.random.randint(0, 65535)
self.rng = np.random.default_rng(rng_seed)
# convert probabilities from
self.probabilities = np.asarray(list(self.settings.action_probabilities.values()))
super().__init__(agent_name, action_space, observation_space, reward_function)
self.logger.debug(f"ProbabilisticAgent RNG seed: {rng_seed}")
@property
def probabilities(self) -> Dict[str, int]:
"""Convenience method to view the probabilities of the Agent."""
return np.asarray(list(self.config.action_probabilities.values()))
def get_action(self, obs: ObsType, timestep: int = 0) -> Tuple[str, Dict]:
"""
Sample the action space randomly.

73
src/primaite/game/agent/scripted_agents/tap001.py
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@@ -1,73 +0,0 @@
# © Crown-owned copyright 2024, Defence Science and Technology Laboratory UK
from __future__ import annotations
import random
from typing import Dict, Tuple
from primaite.game.agent.scripted_agents.abstract_tap import AbstractTAPAgent
class TAP001(AbstractTAPAgent, identifier="TAP001"):
"""
TAP001 | Mobile Malware -- Ransomware Variant.
Scripted Red Agent. Capable of one action; launching the kill-chain (Ransomware Application)
"""
config: "TAP001.ConfigSchema"
class ConfigSchema(AbstractTAPAgent.ConfigSchema):
"""Configuration Schema for TAP001 Agent."""
agent_name: str = "TAP001"
installed: bool = False
def __init__(self) -> None:
"""___init___ bruv. Restecpa."""
super().__init__()
self.setup_agent()
@property
def starting_node_name(self) -> str:
"""Node that TAP001 starts from."""
return self.config.starting_node_name
def get_action(self, timestep: int) -> Tuple[str, Dict]:
"""Waits until a specific timestep, then attempts to execute the ransomware application.
This application acts a wrapper around the kill-chain, similar to green-analyst and
the previous UC2 data manipulation bot.
:param timestep: The current simulation timestep, used for scheduling actions
:type timestep: int
:return: Action formatted in CAOS format
:rtype: Tuple[str, Dict]
"""
if timestep < self.config.next_execution_timestep:
return "do_nothing", {}
self._set_next_execution_timestep(timestep + self.config.agent_settings.start_settings.frequency)
if not self.config.installed:
self.config.installed = True
return "node_application_install", {
"node_name": self.starting_node_name,
"application_name": "RansomwareScript",
}
return "node_application_execute", {
"node_name": self.starting_node_name,
"application_name": "RansomwareScript",
}
def setup_agent(self) -> None:
"""Set the next execution timestep when the episode resets."""
self._select_start_node()
self._set_next_execution_timestep(self.config.agent_settings.start_settings.start_step)
for n, act in self.config.action_manager.action_map.items():
if not act[0] == "node_application_install":
continue
if act[1]["node_name"] == self.starting_node_name:
self.ip_address = act[1]["ip_address"]
return
raise RuntimeError("TAP001 agent could not find database server ip address in action map")