Move IO to environments from session and add agent logging

src/primaite/config/_package_data/example_config.yaml

@@ -13,6 +13,7 @@ training_config:
 io_settings:
   save_checkpoints: true
   checkpoint_interval: 5
+  save_agent_actions: true
   save_step_metadata: false
   save_pcap_logs: false
   save_sys_logs: true

src/primaite/config/_package_data/example_config_2_rl_agents.yaml

@@ -15,6 +15,7 @@ training_config:
 io_settings:
   save_checkpoints: true
   checkpoint_interval: 5
+  save_agent_actions: true
   save_step_metadata: false
   save_pcap_logs: false
   save_sys_logs: true
@@ -36,6 +37,11 @@ agents:
   - ref: client_2_green_user
     team: GREEN
     type: ProbabilisticAgent
+    agent_settings:
+      action_probabilities:
+        0: 0.3
+        1: 0.6
+        2: 0.1
     observation_space:
       type: UC2GreenObservation
     action_space:
@@ -47,24 +53,38 @@ agents:
         - node_name: client_2
           applications:
             - application_name: WebBrowser
+            - application_name: DatabaseClient
         max_folders_per_node: 1
         max_files_per_folder: 1
         max_services_per_node: 1
-        max_applications_per_node: 1
+        max_applications_per_node: 2
+      action_map:
+        0:
+          action: DONOTHING
+          options: {}
+        1:
+          action: NODE_APPLICATION_EXECUTE
+          options:
+            node_id: 0
+            application_id: 0
+        2:
+          action: NODE_APPLICATION_EXECUTE
+          options:
+            node_id: 0
+            application_id: 1
 
     reward_function:
       reward_components:
         - type: DUMMY
 
-    agent_settings:
-      start_settings:
-        start_step: 5
-        frequency: 4
-        variance: 3
-
   - ref: client_1_green_user
     team: GREEN
     type: ProbabilisticAgent
+    agent_settings:
+      action_probabilities:
+        0: 0.3
+        1: 0.6
+        2: 0.1
     observation_space:
       type: UC2GreenObservation
     action_space:
@@ -76,10 +96,26 @@ agents:
           - node_name: client_1
             applications:
               - application_name: WebBrowser
+              - application_name: DatabaseClient
         max_folders_per_node: 1
         max_files_per_folder: 1
         max_services_per_node: 1
-        max_applications_per_node: 1
+        max_applications_per_node: 2
+      action_map:
+        0:
+          action: DONOTHING
+          options: {}
+        1:
+          action: NODE_APPLICATION_EXECUTE
+          options:
+            node_id: 0
+            application_id: 0
+        2:
+          action: NODE_APPLICATION_EXECUTE
+          options:
+            node_id: 0
+            application_id: 1
+
     reward_function:
       reward_components:
         - type: DUMMY
@@ -1036,7 +1072,6 @@ agents:
 
 
 
-
 simulation:
   network:
     nmne_config:
@@ -1046,8 +1081,8 @@ simulation:
     nodes:
 
     - ref: router_1
-      type: router
       hostname: router_1
+      type: router
       num_ports: 5
       ports:
         1:
@@ -1082,18 +1117,18 @@ simulation:
           protocol: ICMP
 
     - ref: switch_1
-      type: switch
       hostname: switch_1
+      type: switch
       num_ports: 8
 
     - ref: switch_2
-      type: switch
       hostname: switch_2
+      type: switch
       num_ports: 8
 
     - ref: domain_controller
-      type: server
       hostname: domain_controller
+      type: server
       ip_address: 192.168.1.10
       subnet_mask: 255.255.255.0
       default_gateway: 192.168.1.1
@@ -1105,8 +1140,8 @@ simulation:
             arcd.com: 192.168.1.12 # web server
 
     - ref: web_server
-      type: server
       hostname: web_server
+      type: server
       ip_address: 192.168.1.12
       subnet_mask: 255.255.255.0
       default_gateway: 192.168.1.1
@@ -1122,8 +1157,8 @@ simulation:
 
 
     - ref: database_server
-      type: server
       hostname: database_server
+      type: server
       ip_address: 192.168.1.14
       subnet_mask: 255.255.255.0
       default_gateway: 192.168.1.1
@@ -1137,8 +1172,8 @@ simulation:
         type: FTPClient
 
     - ref: backup_server
-      type: server
       hostname: backup_server
+      type: server
       ip_address: 192.168.1.16
       subnet_mask: 255.255.255.0
       default_gateway: 192.168.1.1
@@ -1148,8 +1183,8 @@ simulation:
         type: FTPServer
 
     - ref: security_suite
-      type: server
       hostname: security_suite
+      type: server
       ip_address: 192.168.1.110
       subnet_mask: 255.255.255.0
       default_gateway: 192.168.1.1
@@ -1160,8 +1195,8 @@ simulation:
           subnet_mask: 255.255.255.0
 
     - ref: client_1
-      type: computer
       hostname: client_1
+      type: computer
       ip_address: 192.168.10.21
       subnet_mask: 255.255.255.0
       default_gateway: 192.168.10.1
@@ -1178,13 +1213,17 @@ simulation:
         type: WebBrowser
         options:
           target_url: http://arcd.com/users/
+      - ref: client_1_database_client
+        type: DatabaseClient
+        options:
+          db_server_ip: 192.168.1.14
       services:
       - ref: client_1_dns_client
         type: DNSClient
 
     - ref: client_2
-      type: computer
       hostname: client_2
+      type: computer
       ip_address: 192.168.10.22
       subnet_mask: 255.255.255.0
       default_gateway: 192.168.10.1
@@ -1201,6 +1240,10 @@ simulation:
           data_manipulation_p_of_success: 0.8
           payload: "DELETE"
           server_ip: 192.168.1.14
+      - ref: client_2_database_client
+        type: DatabaseClient
+        options:
+          db_server_ip: 192.168.1.14
       services:
       - ref: client_2_dns_client
         type: DNSClient

src/primaite/game/agent/data_manipulation_bot.py

@@ -37,7 +37,7 @@ class DataManipulationAgent(AbstractScriptedAgent):
         :rtype: Tuple[str, Dict]
         """
         if timestep < self.next_execution_timestep:
-            return "DONOTHING", {"dummy": 0}
+            return "DONOTHING", {}
 
         self._set_next_execution_timestep(timestep + self.agent_settings.start_settings.frequency)
 

src/primaite/game/game.py

@@ -11,7 +11,6 @@ from primaite.game.agent.interface import AbstractAgent, AgentSettings, ProxyAge
 from primaite.game.agent.observations import ObservationManager
 from primaite.game.agent.rewards import RewardFunction
 from primaite.game.agent.scripted_agents import ProbabilisticAgent
-from primaite.session.io import SessionIO, SessionIOSettings
 from primaite.simulator.network.hardware.base import NodeOperatingState
 from primaite.simulator.network.hardware.nodes.host.computer import Computer
 from primaite.simulator.network.hardware.nodes.host.host_node import NIC
@@ -210,10 +209,6 @@ class PrimaiteGame:
         :return: A PrimaiteGame object.
         :rtype: PrimaiteGame
         """
-        io_settings = cfg.get("io_settings", {})
-        _ = SessionIO(SessionIOSettings(**io_settings))
-        # Instantiating this ensures that the game saves to the correct output dir even without being part of a session
-
         game = cls()
         game.options = PrimaiteGameOptions(**cfg["game"])
         game.save_step_metadata = cfg.get("io_settings", {}).get("save_step_metadata") or False
@@ -415,7 +410,7 @@ class PrimaiteGame:
             # CREATE AGENT
             if agent_type == "ProbabilisticAgent":
                 # TODO: implement non-random agents and fix this parsing
-                settings = agent_cfg.get("agent_settings")
+                settings = agent_cfg.get("agent_settings", {})
                 new_agent = ProbabilisticAgent(
                     agent_name=agent_cfg["ref"],
                     action_space=action_space,

src/primaite/session/environment.py

@@ -8,6 +8,7 @@ from ray.rllib.env.multi_agent_env import MultiAgentEnv
 
 from primaite.game.agent.interface import ProxyAgent
 from primaite.game.game import PrimaiteGame
+from primaite.session.io import PrimaiteIO
 from primaite.simulator import SIM_OUTPUT
 
 
@@ -32,6 +33,9 @@ class PrimaiteGymEnv(gymnasium.Env):
         self.episode_counter: int = 0
         """Current episode number."""
 
+        self.io = PrimaiteIO.from_config(game_config.get("io_settings", {}))
+        """Handles IO for the environment. This produces sys logs, agent logs, etc."""
+
     @property
     def agent(self) -> ProxyAgent:
         """Grab a fresh reference to the agent object because it will be reinstantiated each episode."""
@@ -55,6 +59,10 @@ class PrimaiteGymEnv(gymnasium.Env):
         info = {"agent_actions": agent_actions}  # tell us what all the agents did for convenience.
         if self.game.save_step_metadata:
             self._write_step_metadata_json(action, state, reward)
+        if self.io.settings.save_agent_actions:
+            self.io.store_agent_actions(
+                agent_actions=agent_actions, episode=self.episode_counter, timestep=self.game.step_counter
+            )
         return next_obs, reward, terminated, truncated, info
 
     def _write_step_metadata_json(self, action: int, state: Dict, reward: int):
@@ -79,6 +87,9 @@ class PrimaiteGymEnv(gymnasium.Env):
             f"Resetting environment, episode {self.episode_counter}, "
             f"avg. reward: {self.agent.reward_function.total_reward}"
         )
+        if self.io.settings.save_agent_actions:
+            self.io.write_agent_actions(episode=self.episode_counter)
+            self.io.clear_agent_actions()
         self.game: PrimaiteGame = PrimaiteGame.from_config(cfg=copy.deepcopy(self.game_config))
         self.game.setup_for_episode(episode=self.episode_counter)
         self.episode_counter += 1
@@ -146,7 +157,7 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
         """
         self.game_config: Dict = env_config
         """PrimaiteGame definition. This can be changed between episodes to enable curriculum learning."""
-        self.game: PrimaiteGame = PrimaiteGame.from_config(self.game_config)
+        self.game: PrimaiteGame = PrimaiteGame.from_config(copy.deepcopy(self.game_config))
         """Reference to the primaite game"""
         self._agent_ids = list(self.game.rl_agents.keys())
         """Agent ids. This is a list of strings of agent names."""
@@ -164,6 +175,10 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
         self.action_space = gymnasium.spaces.Dict(
             {name: agent.action_manager.space for name, agent in self.agents.items()}
         )
+
+        self.io = PrimaiteIO.from_config(env_config.get("io_settings"))
+        """Handles IO for the environment. This produces sys logs, agent logs, etc."""
+
         super().__init__()
 
     @property
@@ -173,7 +188,10 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
 
     def reset(self, *, seed: int = None, options: dict = None) -> Tuple[ObsType, Dict]:
         """Reset the environment."""
-        self.game: PrimaiteGame = PrimaiteGame.from_config(cfg=self.game_config)
+        if self.io.settings.save_agent_actions:
+            self.io.write_agent_actions(episode=self.episode_counter)
+            self.io.clear_agent_actions()
+        self.game: PrimaiteGame = PrimaiteGame.from_config(cfg=copy.deepcopy(self.game_config))
         self.game.setup_for_episode(episode=self.episode_counter)
         self.episode_counter += 1
         state = self.game.get_sim_state()
@@ -196,7 +214,7 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
         # 1. Perform actions
         for agent_name, action in actions.items():
             self.agents[agent_name].store_action(action)
-        self.game.apply_agent_actions()
+        agent_actions = self.game.apply_agent_actions()
 
         # 2. Advance timestep
         self.game.advance_timestep()
@@ -215,6 +233,10 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
         truncateds["__all__"] = self.game.calculate_truncated()
         if self.game.save_step_metadata:
             self._write_step_metadata_json(actions, state, rewards)
+        if self.io.settings.save_agent_actions:
+            self.io.store_agent_actions(
+                agent_actions=agent_actions, episode=self.episode_counter, timestep=self.game.step_counter
+            )
         return next_obs, rewards, terminateds, truncateds, infos
 
     def _write_step_metadata_json(self, actions: Dict, state: Dict, rewards: Dict):

src/primaite/session/io.py

@@ -1,30 +1,34 @@
+import json
 from datetime import datetime
 from pathlib import Path
-from typing import Optional
+from typing import Dict, List, Optional
 
 from pydantic import BaseModel, ConfigDict
 
-from primaite import PRIMAITE_PATHS
+from primaite import getLogger, PRIMAITE_PATHS
 from primaite.simulator import SIM_OUTPUT
 
+_LOGGER = getLogger(__name__)
 
-class SessionIOSettings(BaseModel):
+
-    """Schema for session IO settings."""
+class PrimaiteIO:
+    """
+    Class for managing session IO.
+
+    Currently it's handling path generation, but could expand to handle loading, transaction, tensorboard, and so on.
+    """
+
+    class Settings(BaseModel):
+        """Config schema for PrimaiteIO object."""
 
         model_config = ConfigDict(extra="forbid")
 
-    save_final_model: bool = True
-    """Whether to save the final model right at the end of training."""
-    save_checkpoints: bool = False
-    """Whether to save a checkpoint model every `checkpoint_interval` episodes"""
-    checkpoint_interval: int = 10
-    """How often to save a checkpoint model (if save_checkpoints is True)."""
         save_logs: bool = True
         """Whether to save logs"""
+        save_agent_actions: bool = True
+        """Whether to save a log of all agents' actions every step."""
         save_transactions: bool = True
         """Whether to save transactions, If true, the session path will have a transactions folder."""
-    save_tensorboard_logs: bool = False
-    """Whether to save tensorboard logs. If true, the session path will have a tensorboard_logs folder."""
         save_step_metadata: bool = False
         """Whether to save the RL agents' action, environment state, and other data at every single step."""
         save_pcap_logs: bool = False
@@ -32,22 +36,15 @@ class SessionIOSettings(BaseModel):
         save_sys_logs: bool = False
         """Whether to save system logs."""
 
-
+    def __init__(self, settings: Optional[Settings] = None) -> None:
-class SessionIO:
+        self.settings = settings or PrimaiteIO.Settings()
-    """
-    Class for managing session IO.
-
-    Currently it's handling path generation, but could expand to handle loading, transaction, tensorboard, and so on.
-    """
-
-    def __init__(self, settings: SessionIOSettings = SessionIOSettings()) -> None:
-        self.settings: SessionIOSettings = settings
         self.session_path: Path = self.generate_session_path()
         # set global SIM_OUTPUT path
         SIM_OUTPUT.path = self.session_path / "simulation_output"
         SIM_OUTPUT.save_pcap_logs = self.settings.save_pcap_logs
         SIM_OUTPUT.save_sys_logs = self.settings.save_sys_logs
 
+        self.agent_action_log: List[Dict] = []
         # warning TODO: must be careful not to re-initialise sessionIO because it will create a new path each time it's
         # possible refactor needed
 
@@ -68,3 +65,56 @@ class SessionIO:
     def generate_checkpoint_save_path(self, agent_name: str, episode: int) -> Path:
         """Return the path where the checkpoint model will be saved (excluding filename extension)."""
         return self.session_path / "checkpoints" / f"{agent_name}_checkpoint_{episode}.pt"
+
+    def generate_agent_actions_save_path(self, episode: int) -> Path:
+        """Return the path where agent actions will be saved."""
+        return self.session_path / "agent_actions" / f"episode_{episode}.json"
+
+    def store_agent_actions(self, agent_actions: Dict, episode: int, timestep: int) -> None:
+        """Cache agent actions for a particular step.
+
+        :param agent_actions: Dictionary describing actions for any agents that acted in this timestep. The expected
+            format contains agent identifiers as keys. The keys should map to a tuple of [CAOS action, parameters]
+            CAOS action is a string representing one the CAOS actions.
+            parameters is a dict of parameter names and values for that particular CAOS action.
+            For example:
+                {
+                    'green1' : ('NODE_APPLICATION_EXECUTE', {'node_id':1, 'application_id':0}),
+                    'defender': ('DO_NOTHING', {})
+                }
+        :type agent_actions: Dict
+        :param timestep: Simulation timestep when these actions occurred.
+        :type timestep: int
+        """
+        self.agent_action_log.append(
+            [
+                {
+                    "episode": episode,
+                    "timestep": timestep,
+                    "agent_actions": {k: {"action": v[0], "parameters": v[1]} for k, v in agent_actions.items()},
+                }
+            ]
+        )
+
+    def write_agent_actions(self, episode: int) -> None:
+        """Take the contents of the agent action log and write it to a file.
+
+        :param episode: Episode number
+        :type episode: int
+        """
+        path = self.generate_agent_actions_save_path(episode=episode)
+        path.parent.mkdir(exist_ok=True, parents=True)
+        path.touch()
+        _LOGGER.info(f"Saving agent action log to {path}")
+        with open(path, "w") as file:
+            json.dump(self.agent_action_log, fp=file, indent=1)
+
+    def clear_agent_actions(self) -> None:
+        """Reset the agent action log back to an empty dictionary."""
+        self.agent_action_log = []
+
+    @classmethod
+    def from_config(cls, config: Dict) -> "PrimaiteIO":
+        """Create an instance of PrimaiteIO based on a configuration dict."""
+        new = cls()
+        return new

src/primaite/session/policy/sb3.py

@@ -39,9 +39,9 @@ class SB3Policy(PolicyABC, identifier="SB3"):
 
     def learn(self, n_episodes: int, timesteps_per_episode: int) -> None:
         """Train the agent."""
-        if self.session.io_manager.settings.save_checkpoints:
+        if self.session.save_checkpoints:
             checkpoint_callback = CheckpointCallback(
-                save_freq=timesteps_per_episode * self.session.io_manager.settings.checkpoint_interval,
+                save_freq=timesteps_per_episode * self.session.checkpoint_interval,
                 save_path=self.session.io_manager.generate_model_save_path("sb3"),
                 name_prefix="sb3_model",
             )

src/primaite/session/session.py

@@ -1,3 +1,4 @@
+# raise DeprecationWarning("This module is deprecated")
 from enum import Enum
 from pathlib import Path
 from typing import Dict, List, Literal, Optional, Union
@@ -5,7 +6,7 @@ from typing import Dict, List, Literal, Optional, Union
 from pydantic import BaseModel, ConfigDict
 
 from primaite.session.environment import PrimaiteGymEnv, PrimaiteRayEnv, PrimaiteRayMARLEnv
-from primaite.session.io import SessionIO, SessionIOSettings
+from primaite.session.io import PrimaiteIO
 
 # from primaite.game.game import PrimaiteGame
 from primaite.session.policy.policy import PolicyABC
@@ -53,12 +54,18 @@ class PrimaiteSession:
         self.policy: PolicyABC
         """The reinforcement learning policy."""
 
-        self.io_manager: Optional["SessionIO"] = None
+        self.io_manager: Optional["PrimaiteIO"] = None
         """IO manager for the session."""
 
         self.game_cfg: Dict = game_cfg
         """Primaite Game object for managing main simulation loop and agents."""
 
+        self.save_checkpoints: bool = False
+        """Whether to save chcekpoints."""
+
+        self.checkpoint_interval: int = 10
+        """If save_checkpoints is true, checkpoints will be saved every checkpoint_interval episodes."""
+
     def start_session(self) -> None:
         """Commence the training/eval session."""
         print("Starting Primaite Session")
@@ -89,12 +96,13 @@ class PrimaiteSession:
     def from_config(cls, cfg: Dict, agent_load_path: Optional[str] = None) -> "PrimaiteSession":
         """Create a PrimaiteSession object from a config dictionary."""
         # READ IO SETTINGS (this sets the global session path as well) # TODO: GLOBAL SIDE EFFECTS...
-        io_settings = cfg.get("io_settings", {})
+        io_manager = PrimaiteIO.from_config(cfg.get("io_settings", {}))
-        io_manager = SessionIO(SessionIOSettings(**io_settings))
 
         sess = cls(game_cfg=cfg)
         sess.io_manager = io_manager
         sess.training_options = TrainingOptions(**cfg["training_config"])
+        sess.save_checkpoints = cfg.get("io_settings", {}).get("save_checkpoints")
+        sess.checkpoint_interval = cfg.get("io_settings", {}).get("checkpoint_interval")
 
         # CREATE ENVIRONMENT
         if sess.training_options.rl_framework == "RLLIB_single_agent":