#1595: Moved hardcoded agent into its own file

src/primaite/agents/agent.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import json
-import time
 from abc import ABC, abstractmethod
 from datetime import datetime
 from pathlib import Path
@@ -308,103 +307,3 @@ class AgentSessionABC(ABC):
         fig = plot_av_reward_per_episode(path, title, subtitle)
         fig.write_image(image_path)
         _LOGGER.debug(f"Saved average rewards per episode plot to: {path}")
-
-
-class HardCodedAgentSessionABC(AgentSessionABC):
-    """
-    An Agent Session ABC for evaluation deterministic agents.
-
-    This class cannot be directly instantiated and must be inherited from with all implemented abstract methods
-    implemented.
-    """
-
-    def __init__(self, training_config_path, lay_down_config_path):
-        """
-        Initialise a hardcoded agent session.
-
-        :param training_config_path: YAML file containing configurable items defined in
-            `primaite.config.training_config.TrainingConfig`
-        :type training_config_path: Union[path, str]
-        :param lay_down_config_path: YAML file containing configurable items for generating network laydown.
-        :type lay_down_config_path: Union[path, str]
-        """
-        super().__init__(training_config_path, lay_down_config_path)
-        self._setup()
-
-    def _setup(self):
-        self._env: Primaite = Primaite(
-            training_config_path=self._training_config_path,
-            lay_down_config_path=self._lay_down_config_path,
-            session_path=self.session_path,
-            timestamp_str=self.timestamp_str,
-        )
-        super()._setup()
-        self._can_learn = False
-        self._can_evaluate = True
-
-    def _save_checkpoint(self):
-        pass
-
-    def _get_latest_checkpoint(self):
-        pass
-
-    def learn(
-        self,
-        **kwargs,
-    ):
-        """
-        Train the agent.
-
-        :param kwargs: Any agent-specific key-word args to be passed.
-        """
-        _LOGGER.warning("Deterministic agents cannot learn")
-
-    @abstractmethod
-    def _calculate_action(self, obs):
-        pass
-
-    def evaluate(
-        self,
-        **kwargs,
-    ):
-        """
-        Evaluate the agent.
-
-        :param kwargs: Any agent-specific key-word args to be passed.
-        """
-        self._env.set_as_eval()  # noqa
-        self.is_eval = True
-
-        time_steps = self._training_config.num_steps
-        episodes = self._training_config.num_episodes
-
-        obs = self._env.reset()
-        for episode in range(episodes):
-            # Reset env and collect initial observation
-            for step in range(time_steps):
-                # Calculate action
-                action = self._calculate_action(obs)
-
-                # Perform the step
-                obs, reward, done, info = self._env.step(action)
-
-                if done:
-                    break
-
-                # Introduce a delay between steps
-                time.sleep(self._training_config.time_delay / 1000)
-            obs = self._env.reset()
-        self._env.close()
-
-    @classmethod
-    def load(cls):
-        """Load an agent from file."""
-        _LOGGER.warning("Deterministic agents cannot be loaded")
-
-    def save(self):
-        """Save the agent."""
-        _LOGGER.warning("Deterministic agents cannot be saved")
-
-    def export(self):
-        """Export the agent to transportable file format."""
-        _LOGGER.warning("Deterministic agents cannot be exported")

src/primaite/agents/hardcoded.py

@@ -0,0 +1,108 @@
+import time
+from abc import abstractmethod
+
+from primaite import getLogger
+from primaite.agents.agent import AgentSessionABC
+from primaite.environment.primaite_env import Primaite
+
+_LOGGER = getLogger(__name__)
+
+
+class HardCodedAgentSessionABC(AgentSessionABC):
+    """
+    An Agent Session ABC for evaluation deterministic agents.
+
+    This class cannot be directly instantiated and must be inherited from with all implemented abstract methods
+    implemented.
+    """
+
+    def __init__(self, training_config_path, lay_down_config_path):
+        """
+        Initialise a hardcoded agent session.
+
+        :param training_config_path: YAML file containing configurable items defined in
+            `primaite.config.training_config.TrainingConfig`
+        :type training_config_path: Union[path, str]
+        :param lay_down_config_path: YAML file containing configurable items for generating network laydown.
+        :type lay_down_config_path: Union[path, str]
+        """
+        super().__init__(training_config_path, lay_down_config_path)
+        self._setup()
+
+    def _setup(self):
+        self._env: Primaite = Primaite(
+            training_config_path=self._training_config_path,
+            lay_down_config_path=self._lay_down_config_path,
+            session_path=self.session_path,
+            timestamp_str=self.timestamp_str,
+        )
+        super()._setup()
+        self._can_learn = False
+        self._can_evaluate = True
+
+    def _save_checkpoint(self):
+        pass
+
+    def _get_latest_checkpoint(self):
+        pass
+
+    def learn(
+        self,
+        **kwargs,
+    ):
+        """
+        Train the agent.
+
+        :param kwargs: Any agent-specific key-word args to be passed.
+        """
+        _LOGGER.warning("Deterministic agents cannot learn")
+
+    @abstractmethod
+    def _calculate_action(self, obs):
+        pass
+
+    def evaluate(
+        self,
+        **kwargs,
+    ):
+        """
+        Evaluate the agent.
+
+        :param kwargs: Any agent-specific key-word args to be passed.
+        """
+        self._env.set_as_eval()  # noqa
+        self.is_eval = True
+
+        time_steps = self._training_config.num_steps
+        episodes = self._training_config.num_episodes
+
+        obs = self._env.reset()
+        for episode in range(episodes):
+            # Reset env and collect initial observation
+            for step in range(time_steps):
+                # Calculate action
+                action = self._calculate_action(obs)
+
+                # Perform the step
+                obs, reward, done, info = self._env.step(action)
+
+                if done:
+                    break
+
+                # Introduce a delay between steps
+                time.sleep(self._training_config.time_delay / 1000)
+            obs = self._env.reset()
+        self._env.close()
+
+    @classmethod
+    def load(cls, path=None):
+        """Load an agent from file."""
+        _LOGGER.warning("Deterministic agents cannot be loaded")
+
+    def save(self):
+        """Save the agent."""
+        _LOGGER.warning("Deterministic agents cannot be saved")
+
+    def export(self):
+        """Export the agent to transportable file format."""
+        _LOGGER.warning("Deterministic agents cannot be exported")

src/primaite/agents/hardcoded_acl.py

@@ -4,7 +4,7 @@ import numpy as np
 
 from primaite.acl.access_control_list import AccessControlList
 from primaite.acl.acl_rule import ACLRule
-from primaite.agents.agent import HardCodedAgentSessionABC
+from primaite.agents.hardcoded import HardCodedAgentSessionABC
 from primaite.agents.utils import (
     get_new_action,
     get_node_of_ip,

src/primaite/agents/hardcoded_node.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from primaite.agents.agent import HardCodedAgentSessionABC
+from primaite.agents.hardcoded import HardCodedAgentSessionABC
 from primaite.agents.utils import get_new_action, transform_action_node_enum, transform_change_obs_readable
 
 

src/primaite/agents/simple.py

@@ -1,4 +1,4 @@
-from primaite.agents.agent import HardCodedAgentSessionABC
+from primaite.agents.hardcoded import HardCodedAgentSessionABC
 from primaite.agents.utils import get_new_action, transform_action_acl_enum, transform_action_node_enum