Rename benchmark variables and add h1 to notebooks

src/primaite/notebooks/Data-Manipulation-Customising-Red-Agent.ipynb

@@ -458,7 +458,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

src/primaite/notebooks/Training-an-RLLIB-MARL-System.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Train a Multi agent system using RLLIB\n",
+    "# Train a Multi agent system using RLLIB\n",
     "\n",
     "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK\n",
     "\n",
@@ -108,7 +108,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

src/primaite/notebooks/Training-an-RLLib-Agent.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Train a Single agent system using RLLib\n",
+    "# Train a Single agent system using RLLib\n",
     "\n",
     "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK\n",
     "\n",
@@ -99,7 +99,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

src/primaite/notebooks/Using-Episode-Schedules.ipynb

@@ -328,7 +328,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

src/primaite/notebooks/multi-processing.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simple multi-processing demonstration\n",
+    "# Simple multi-processing demonstration\n",
     "\n",
     "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK\n",
     "\n",
@@ -143,7 +143,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

src/primaite/session/environment.py

@@ -37,7 +37,7 @@ class PrimaiteGymEnv(gymnasium.Env):
         """Name of the RL agent. Since there should only be one RL agent we can just pull the first and only key."""
         self.episode_counter: int = 0
         """Current episode number."""
-        self.average_reward_per_episode: Dict[int, float] = {}
+        self.total_reward_per_episode: Dict[int, float] = {}
         """Average rewards of agents per episode."""
 
     @property
@@ -91,7 +91,7 @@ class PrimaiteGymEnv(gymnasium.Env):
             f"Resetting environment, episode {self.episode_counter}, "
             f"avg. reward: {self.agent.reward_function.total_reward}"
         )
-        self.average_reward_per_episode[self.episode_counter] = self.agent.reward_function.total_reward
+        self.total_reward_per_episode[self.episode_counter] = self.agent.reward_function.total_reward
 
         if self.io.settings.save_agent_actions:
             all_agent_actions = {name: agent.history for name, agent in self.game.agents.items()}

src/primaite/utils/session_output_reader.py

@@ -11,16 +11,16 @@ from typing import Any, Dict, Tuple, Union
 import polars as pl
 
 
-def av_rewards_dict(av_rewards_csv_file: Union[str, Path]) -> Dict[int, float]:
+def total_rewards_dict(total_rewards_csv_file: Union[str, Path]) -> Dict[int, float]:
     """
     Read an average rewards per episode csv file and return as a dict.
 
     The dictionary keys are the episode number, and the values are the mean reward that episode.
 
-    :param av_rewards_csv_file: The average rewards per episode csv file path.
+    :param total_rewards_csv_file: The average rewards per episode csv file path.
     :return: The average rewards per episode csv as a dict.
     """
-    df_dict = pl.read_csv(av_rewards_csv_file).to_dict()
+    df_dict = pl.read_csv(total_rewards_csv_file).to_dict()
 
     return {int(v): df_dict["Average Reward"][i] for i, v in enumerate(df_dict["Episode"])}
 

src/primaite/utils/session_output_writer.py

@@ -26,9 +26,9 @@ class SessionOutputWriter:
     Is used to write session outputs to csv file.
     """
 
-    _AV_REWARD_PER_EPISODE_HEADER: Final[List[str]] = [
+    _TOTAL_REWARD_PER_EPISODE_HEADER: Final[List[str]] = [
         "Episode",
-        "Average Reward",
+        "Total Reward",
     ]
 
     def __init__(
@@ -43,7 +43,7 @@ class SessionOutputWriter:
         :param env: PrimAITE gym environment.
         :type env: Primaite
         :param transaction_writer: If `true`, this will output a full account of every transaction taken by the agent.
-            If `false` it will output the average reward per episode, defaults to False
+            If `false` it will output the total reward per episode, defaults to False
         :type transaction_writer: bool, optional
         :param learning_session: Set to `true` to indicate that the current session is a training session. This
             determines the name of the folder which contains the final output csv. Defaults to True
@@ -56,7 +56,7 @@ class SessionOutputWriter:
         if self.transaction_writer:
             fn = f"all_transactions_{self._env.timestamp_str}.csv"
         else:
-            fn = f"average_reward_per_episode_{self._env.timestamp_str}.csv"
+            fn = f"total_reward_per_episode_{self._env.timestamp_str}.csv"
 
         self._csv_file_path: "Path"
         if self.learning_session:
@@ -94,7 +94,7 @@ class SessionOutputWriter:
         if isinstance(data, Transaction):
             header, data = data.as_csv_data()
         else:
-            header = self._AV_REWARD_PER_EPISODE_HEADER
+            header = self._TOTAL_REWARD_PER_EPISODE_HEADER
 
         if self._first_write:
             self._init_csv_writer()