Initialise observations in agent interface

src/primaite/game/actor/actions.py

@@ -0,0 +1,21 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List
+
+from pydantic import BaseModel
+
+
+class AbstractAction(BaseModel):
+    @abstractmethod
+    def __call__(self, action: Any) -> List[str]:
+        """_summary_
+
+        :param action: _description_
+        :type action: Any
+        :return: _description_
+        :rtype: List[str]
+        """
+        ...
+
+
+class ActionSpace:
+    ...

src/primaite/game/actor/interface.py

@@ -0,0 +1,32 @@
+# TODO: remove this comment... This is just here to point out that I've named this 'actor' rather than 'agent'
+# That's because I want to point out that this is disctinct from 'agent' in the reinforcement learning sense of the word
+# If you disagree, make a comment in the PR review and we can discuss
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List
+
+from pydantic import BaseModel
+
+from primaite.game.actor.actions import ActionSpace
+from primaite.game.actor.observations import ObservationSpace
+from primaite.game.actor.rewards import RewardFunction
+
+
+class AbstractActor(BaseModel):
+    """Base class for scripted and RL agents."""
+
+    ...
+
+
+class AbstractScriptedActor(AbstractActor):
+    """Base class for actors which generate their own behaviour."""
+
+    ...
+
+
+class AbstractPuppetActor(AbstractActor):
+    """Base class for actors controlled via external messages, such as RL policies."""
+
+    ...
+
+
+# class AbstractRLActor(AbstractPuppetActor): ??

src/primaite/game/actor/observations.py

@@ -0,0 +1,107 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Hashable, List
+
+from pydantic import BaseModel
+
+
+def access_from_nested_dict(dictionary: Dict, keys: List[Hashable]) -> Any:
+    """
+    Access an item from a deeply dictionary with a list of keys.
+
+    For example, if the dictionary is {1: 'a', 2: {3: {4: 'b'}}}, then the key [2, 3, 4] would return 'b', and the key
+    [2, 3] would return {4: 'b'}. Raises a KeyError if specified key does not exist at any level of nesting.
+
+    :param dictionary: Deeply nested dictionary
+    :type dictionary: Dict
+    :param keys: List of dict keys used to traverse the nested dict. Each item corresponds to one level of depth.
+    :type keys: List[Hashable]
+    :return: The value in the dictionary
+    :rtype: Any
+    """
+    if not keys:
+        return dictionary
+    k = keys.pop(0)
+    try:
+        return access_from_nested_dict(dictionary[k], keys)
+    except (TypeError, KeyError):
+        raise KeyError(f"Cannot find requested key `{k}` in nested dictionary")
+
+
+class AbstractObservation(BaseModel):
+    @abstractmethod
+    def __call__(self, state: Dict) -> Any:
+        """_summary_
+
+        :param state: _description_
+        :type state: Dict
+        :return: _description_
+        :rtype: Any
+        """
+        ...
+        # receive state dict
+
+
+class FileObservation(AbstractObservation):
+    where: List[str]
+    """Store information about where in the simulation state dictionary to find the relevatn information."""
+
+    def __call__(self, state: Dict) -> Dict:
+        file_state = access_from_nested_dict(state, self.where)
+
+
+class ObservationSpace:
+    """Manage the observations of an Actor."""
+
+    ...
+    # what this class does:
+    # keep a list of observations
+    # create observations for an actor from the config
+
+
+# Example YAML file for agent observation space
+"""
+arcd_gate:
+  rl_framework: SB3
+  rl_algo: PPO
+  n_learn_steps: 128
+  n_learn_episodes: 1000
+
+game_layer:
+  agents:
+    - ref: client_1_green_user
+      type: GREEN
+      node_ref: client_1
+      service: WebBrowser
+      pol:
+        - step: 1
+          action: START
+
+    - ref: client_1_data_manip_red_bot
+      node_ref: client_1
+      service: DataManipulationBot
+      execution_definition:
+        - server_ip_address: 192.168.1.10
+        - server_password:
+        - payload: 'ATTACK'
+
+      pol:
+        - step: 75
+          action: EXECUTE
+
+
+
+
+simulation:
+  nodes:
+    - ref: client_1
+      hostname: client_1
+      node_type: Computer
+      ip_address: 192.168.10.100
+      services:
+        - name: DataManipulationBot
+  links:
+    endpoint_a:
+    endpoint_b: 1524552-fgfg4147gdh-25gh4gd
+rewards:
+
+"""

src/primaite/game/actor/rewards.py

@@ -0,0 +1,20 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List
+
+from pydantic import BaseModel
+
+
+class AbstractReward(BaseModel):
+    def __call__(self, states: List[Dict]) -> float:
+        """_summary_
+
+        :param state: _description_
+        :type state: Dict
+        :return: _description_
+        :rtype: float
+        """
+        ...
+
+
+class RewardFunction(BaseModel):
+    ...

src/primaite/game/session.py

@@ -0,0 +1,6 @@
+# What do? Be an entry point for using PrimAITE
+# 1. parse monoconfig
+# 2. craete simulation
+# 3. create actors and configure their actions/observations/rewards/ anything else
+# 4. Create connection with ARCD GATE
+# 5. idk

src/primaite/simulator/file_system/file_system.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import math
 import os.path
 import shutil
+from enum import Enum
 from pathlib import Path
 from typing import Dict, Optional
 
@@ -42,6 +43,14 @@ def convert_size(size_bytes: int) -> str:
     return f"{s} {size_name[i]}"
 
 
+class FileSystemItemHealthStatus(Enum):
+    GOOD = 1
+    COMPROMISED = 2
+    CORRUPT = 3
+    RESTORING = 4
+    REPAIRING = 5
+
+
 class FileSystemItemABC(SimComponent):
     """
     Abstract base class for file system items used in the file system simulation.
@@ -51,6 +60,7 @@ class FileSystemItemABC(SimComponent):
 
     name: str
     "The name of the FileSystemItemABC."
+    health_status: FileSystemItemHealthStatus = FileSystemItemHealthStatus.GOOD
 
     def describe_state(self) -> Dict:
         """
@@ -59,11 +69,7 @@ class FileSystemItemABC(SimComponent):
         :return: Current state of this object and child objects.
         """
         state = super().describe_state()
-        state.update(
-            {
-                "name": self.name,
-            }
-        )
+        state.update({"name": self.name, "health_status": self.health_status.value})
         return state
 
     @property

src/primaite/simulator/network/container.py

@@ -163,8 +163,8 @@ class Network(SimComponent):
         state = super().describe_state()
         state.update(
             {
-                "nodes": {i for i, node in self._node_id_map.items()},
-                "links": {i: link.describe_state() for i, link in self._link_id_map.items()},
+                "nodes": {uuid: node.describe_state() for uuid, node in self.nodes.items()},
+                "links": {uuid: link.describe_state() for uuid, link in self.links.items()},
             }
         )
         return state