Merged PR 544: #2871 - Add show functions to Agents
## Summary This PR introduces a `show_history` function that can be used to view what actions have been taken by an agent over a training session. Be default, this won't show any `DONOTHING` actions. ## Test process  ## Checklist - [X] PR is linked to a **work item** - [X] **acceptance criteria** of linked ticket are met - [X] performed **self-review** of the code - [X] written **tests** for any new functionality added with this PR - [X] updated the **documentation** if this PR changes or adds functionality - [ ] written/updated **design docs** if this PR implements new functionality - [X] updated the **change log** - [X] ran **pre-commit** checks for code style - [X] attended to any **TO-DOs** left in the code Related work items: #2871
This commit is contained in:
@@ -5,10 +5,11 @@ All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [3.3.0] - 2024-09-04
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## [3.4.0]
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### Added
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- Log observation space data by episode and step.
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- Added `show_history` method to Agents, allowing you to view actions taken by an agent per step. By default, `DONOTHING` actions are omitted.
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### Changed
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- ACL's are no longer applied to layer-2 traffic.
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@@ -177,3 +177,4 @@ If ``True``, gymnasium flattening will be performed on the observation space bef
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-----------------
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Agents will record their action log for each step. This is a summary of what the agent did, along with response information from requests within the simulation.
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A summary of the actions taken by the agent can be viewed using the `show_history()` function. By default, this will display all actions taken apart from ``DONOTHING``.
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@@ -4,6 +4,7 @@ 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 prettytable import PrettyTable
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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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@@ -126,6 +127,37 @@ class AbstractAgent(ABC):
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self.history: List[AgentHistoryItem] = []
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self.logger = AgentLog(agent_name)
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def add_agent_action(self, item: AgentHistoryItem, table: PrettyTable) -> PrettyTable:
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"""Update the given table with information from given AgentHistoryItem."""
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node, application = "unknown", "unknown"
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if (node_id := item.parameters.get("node_id")) is not None:
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node = self.action_manager.node_names[node_id]
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if (application_id := item.parameters.get("application_id")) is not None:
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application = self.action_manager.application_names[node_id][application_id]
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if (application_name := item.parameters.get("application_name")) is not None:
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application = application_name
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table.add_row([item.timestep, item.action, node, application, item.response.status])
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return table
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def show_history(self, ignored_actions: Optional[list] = None):
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"""
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Print an agent action provided it's not the DONOTHING action.
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:param ignored_actions: OPTIONAL: List of actions to be ignored when displaying the history.
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If not provided, defaults to ignore DONOTHING actions.
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"""
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if not ignored_actions:
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ignored_actions = ["DONOTHING"]
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table = PrettyTable()
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table.field_names = ["Step", "Action", "Node", "Application", "Response"]
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print(f"Actions for '{self.agent_name}':")
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for item in self.history:
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if item.action in ignored_actions:
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pass
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else:
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table = self.add_agent_action(item=item, table=table)
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print(table)
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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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@@ -1800,6 +1800,16 @@
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"\n",
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"display_obs_diffs(tcp_c2_obs, udp_c2_obs, blue_config_env.game.step_counter)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"env.game.agents[\"CustomC2Agent\"].show_history()"
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]
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}
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],
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"metadata": {
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@@ -675,6 +675,15 @@
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" print(f\"step: {env.game.step_counter}, Red action: {info['agent_actions']['data_manipulation_attacker'].action}, Blue reward:{reward:.2f}\" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"env.game.agents[\"data_manipulation_attacker\"].show_history()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@@ -708,7 +717,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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"version": "3.10.11"
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}
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},
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"nbformat": 4,
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@@ -144,6 +144,49 @@
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"PRIMAITE_CONFIG[\"developer_mode\"][\"enabled\"] = was_enabled\n",
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"PRIMAITE_CONFIG[\"developer_mode\"][\"output_sys_logs\"] = was_syslogs_enabled"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Viewing Agent history\n",
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"\n",
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"It's possible to view the actions carried out by an agent for a given training session using the `show_history()` method. By default, this will be all actions apart from DONOTHING actions."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(data_manipulation_config_path(), 'r') as f:\n",
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" cfg = yaml.safe_load(f)\n",
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"\n",
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"env = PrimaiteGymEnv(env_config=cfg)\n",
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"\n",
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"# Run the training session to generate some resultant data.\n",
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"for i in range(100):\n",
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" env.step(0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Calling `.show_history()` should show us when the Data Manipulation used the `NODE_APPLICATION_EXECUTE` action."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"attacker = env.game.agents[\"data_manipulation_attacker\"]\n",
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"\n",
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"attacker.show_history()"
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]
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}
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],
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"metadata": {
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@@ -162,7 +205,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.8"
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"version": "3.10.11"
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}
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},
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"nbformat": 4,
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