#917 - Began the process of reloading existing agents into the session

src/primaite/agents/agent.py

@@ -1,3 +1,4 @@
+from __future__ import annotations
 import json
 import time
 from abc import ABC, abstractmethod
@@ -6,6 +7,8 @@ from pathlib import Path
 from typing import Optional, Final, Dict, Union
 from uuid import uuid4
 
+import yaml
+
 import primaite
 from primaite import getLogger, SESSIONS_DIR
 from primaite.config import lay_down_config
@@ -58,23 +61,34 @@ class AgentSessionABC(ABC):
         self._agent = None
         self._can_learn: bool = False
         self._can_evaluate: bool = False
+        self.is_eval = False
 
         self._uuid = str(uuid4())
         self.session_timestamp: datetime = datetime.now()
         "The session timestamp"
         self.session_path = _get_session_path(self.session_timestamp)
         "The Session path"
-        self.learning_path = self.session_path / "learning"
-        "The learning outputs path"
-        self.evaluation_path = self.session_path / "evaluation"
-        "The evaluation outputs path"
-        self.checkpoints_path = self.learning_path / "checkpoints"
         self.checkpoints_path.mkdir(parents=True, exist_ok=True)
-        "The Session checkpoints path"
 
-        self.timestamp_str = self.session_timestamp.strftime(
-            "%Y-%m-%d_%H-%M-%S")
-        "The session timestamp as a string"
+    @property
+    def timestamp_str(self) -> str:
+        """The session timestamp as a string."""
+        return self.session_timestamp.strftime("%Y-%m-%d_%H-%M-%S")
+
+    @property
+    def learning_path(self) -> Path:
+        """The learning outputs path."""
+        return self.session_path / "learning"
+
+    @property
+    def evaluation_path(self) -> Path:
+        """The evaluation outputs path."""
+        return self.session_path / "evaluation"
+
+    @property
+    def checkpoints_path(self) -> Path:
+        """The Session checkpoints path."""
+        return self.learning_path / "checkpoints"
 
     @property
     def uuid(self):
@@ -104,8 +118,14 @@ class AgentSessionABC(ABC):
             "uuid": self.uuid,
             "start_datetime": self.session_timestamp.isoformat(),
             "end_datetime": None,
-            "total_episodes": None,
-            "total_time_steps": None,
+            "learning": {
+                "total_episodes": None,
+                "total_time_steps": None
+            },
+            "evaluation": {
+                "total_episodes": None,
+                "total_time_steps": None
+            },
             "env": {
                 "training_config": self._training_config.to_dict(
                     json_serializable=True
@@ -134,8 +154,13 @@ class AgentSessionABC(ABC):
             metadata_dict = json.load(file)
 
         metadata_dict["end_datetime"] = datetime.now().isoformat()
-        metadata_dict["total_episodes"] = self._env.episode_count
-        metadata_dict["total_time_steps"] = self._env.total_step_count
+
+        if not self.is_eval:
+            metadata_dict["learning"]["total_episodes"] = self._env.episode_count  # noqa
+            metadata_dict["learning"]["total_time_steps"] = self._env.total_step_count  # noqa
+        else:
+            metadata_dict["evaluation"]["total_episodes"] = self._env.episode_count  # noqa
+            metadata_dict["evaluation"]["total_time_steps"] = self._env.total_step_count  # noqa
 
         filepath = self.session_path / "session_metadata.json"
         _LOGGER.debug(f"Updating Session Metadata file: {filepath}")
@@ -172,6 +197,7 @@ class AgentSessionABC(ABC):
             _LOGGER.debug("Writing transactions")
             self._update_session_metadata_file()
             self._can_evaluate = True
+            self.is_eval = False
 
     @abstractmethod
     def evaluate(
@@ -180,6 +206,7 @@ class AgentSessionABC(ABC):
             episodes: Optional[int] = None,
             **kwargs
     ):
+        self.is_eval = True
         _LOGGER.info("Finished evaluation")
 
     @abstractmethod
@@ -188,7 +215,40 @@ class AgentSessionABC(ABC):
 
     @classmethod
     @abstractmethod
-    def load(cls):
+    def load(cls, path: Union[str, Path]) -> AgentSessionABC:
+        if not isinstance(path, Path):
+            path = Path(path)
+
+        if path.exists():
+            # Unpack the session_metadata.json file
+            md_file = path / "session_metadata.json"
+            with open(md_file, "r") as file:
+                md_dict = json.load(file)
+
+            # Create a temp directory and dump the training and lay down
+            # configs into it
+            temp_dir = path / ".temp"
+            temp_dir.mkdir(exist_ok=True)
+
+            temp_tc = temp_dir / "tc.yaml"
+            with open(temp_tc, "w") as file:
+                yaml.dump(md_dict["env"]["training_config"], file)
+
+            temp_ldc = temp_dir / "ldc.yaml"
+            with open(temp_ldc, "w") as file:
+                yaml.dump(md_dict["env"]["lay_down_config"], file)
+
+            agent = cls(temp_tc, temp_ldc)
+
+            agent.session_path = path
+
+            return agent
+
+        else:
+            # Session path does not exist
+            msg = f"Failed to load PrimAITE Session, path does not exist: {path}"
+            _LOGGER.error(msg)
+            raise FileNotFoundError(msg)
         pass
 
     @abstractmethod

src/primaite/agents/sb3.py

@@ -44,6 +44,8 @@ class SB3Agent(AgentSessionABC):
             f"{self._training_config.agent_identifier}"
         )
 
+        self.is_eval = False
+
     def _setup(self):
         super()._setup()
         self._env = Primaite(
@@ -86,6 +88,7 @@ class SB3Agent(AgentSessionABC):
 
         if not episodes:
             episodes = self._training_config.num_episodes
+        self.is_eval = False
         _LOGGER.info(f"Beginning learning for {episodes} episodes @"
                      f" {time_steps} time steps...")
         for i in range(episodes):
@@ -108,6 +111,7 @@ class SB3Agent(AgentSessionABC):
         if not episodes:
             episodes = self._training_config.num_episodes
         self._env.set_as_eval()
+        self.is_eval = True
         if deterministic:
             deterministic_str = "deterministic"
         else:

src/primaite/config/_package_data/training/training_config_main.yaml

@@ -35,7 +35,7 @@ hard_coded_agent_view: FULL
 # "NODE"
 # "ACL"
 # "ANY" node and acl actions
-action_type: ANY
+action_type: NODE
 
 # Number of episodes to run per session
 num_episodes: 1000

src/primaite/environment/primaite_env.py

@@ -260,6 +260,8 @@ class Primaite(Env):
             learning_session=False
         )
         self.episode_count = 0
+        self.step_count = 0
+        self.total_step_count = 0
 
     def reset(self):
         """
@@ -329,8 +331,6 @@ class Primaite(Env):
         # Load the action space into the transaction
         transaction.action_space = copy.deepcopy(action)
 
-        initial_nodes = copy.deepcopy(self.nodes)
-
         # 1. Implement Blue Action
         self.interpret_action_and_apply(action)
         # Take snapshots of nodes and links
@@ -383,7 +383,7 @@ class Primaite(Env):
 
         # 5. Calculate reward signal (for RL)
         reward = calculate_reward_function(
-            initial_nodes,
+            self.nodes_post_pol,
             self.nodes_post_red,
             self.nodes_reference,
             self.green_iers,