User guide updates

docs/source/varying_config_files.rst

@@ -24,7 +24,7 @@ For each variation that could be used in a placeholder, there is a separate yaml
 
 The data that fills the placeholder is defined as a YAML Anchor in a separate file, denoted by an ampersand ``&anchor``.
 
-Learn more about YAML Aliases and Anchors here.
+Learn more about YAML Aliases and Anchors `here <https://yaml.org/spec/1.2.2/#3222-anchors-and-aliases>`_.
 
 Schedule
 ********

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

@@ -4,13 +4,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Customising red agents\n",
+    "# Customising UC2 Data Manipulation Red Agent\n",
+    "\n",
+    "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK\n",
     "\n",
     "This notebook will go over some examples of how red agent behaviour can be varied by changing its configuration parameters.\n",
     "\n",
     "First, let's load the standard Data Manipulation config file, and see what the red agent does.\n",
     "\n",
-    "*(For a full explanation of the Data Manipulation scenario, check out the notebook `Data-Manipulation-E2E-Demonstration.ipynb`)*"
+    "*(For a full explanation of the Data Manipulation scenario, check out the notebook Data Manipulation Scearnio notebook)*"
    ]
   },
   {
@@ -456,7 +458,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,

src/primaite/notebooks/Data-Manipulation-E2E-Demonstration.ipynb

@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Data Manipulation Scenario\n"
+    "# Data Manipulation Scenario\n",
+    "\n",
+    "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK"
    ]
   },
   {
@@ -79,7 +81,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Reinforcement learning details"
+    "## Reinforcement learning details"
    ]
   },
   {
@@ -692,7 +694,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },

src/primaite/notebooks/Getting-Information-Out-Of-PrimAITE.ipynb

@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Getting information out of PrimAITE"
+    "# Getting information out of PrimAITE\n",
+    "\n",
+    "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK"
    ]
   },
   {

src/primaite/notebooks/Requests-and-Responses.ipynb

@@ -6,7 +6,9 @@
    "source": [
     "# Requests and Responses\n",
     "\n",
-    "Agents interact with the PrimAITE simulation via the Request system.\n"
+    "Agents interact with the PrimAITE simulation via the Request system.\n",
+    "\n",
+    "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK"
    ]
   },
   {

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

@@ -4,7 +4,9 @@
    "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",
     "This notebook will demonstrate how to use the `PrimaiteRayMARLEnv` to train a very basic system with two PPO agents."
    ]
@@ -106,7 +108,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

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

@@ -5,6 +5,9 @@
    "metadata": {},
    "source": [
     "## Train a Single agent system using RLLib\n",
+    "\n",
+    "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK\n",
+    "\n",
     "This notebook will demonstrate how to use PrimaiteRayEnv to train a basic PPO agent."
    ]
   },
@@ -96,7 +99,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

src/primaite/notebooks/Training-an-SB3-Agent.ipynb

@@ -6,6 +6,8 @@
    "source": [
     "# Training an SB3 Agent\n",
     "\n",
+    "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK\n",
+    "\n",
     "This notebook will demonstrate how to use primaite to create and train a PPO agent, using a pre-defined configuration file."
    ]
   },
@@ -180,7 +182,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

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

@@ -6,6 +6,8 @@
    "source": [
     "# Using Episode Schedules\n",
     "\n",
+    "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK\n",
+    "\n",
     "PrimAITE supports the ability to use different variations on a scenario at different episodes. This can be used to increase \n",
     "domain randomisation to prevent overfitting, or to set up curriculum learning to train agents to perform more complicated tasks.\n",
     "\n",
@@ -326,7 +328,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,

src/primaite/notebooks/multi-processing.ipynb

@@ -4,7 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simple multi-processing demo using SubprocVecEnv from SB3"
+    "# Simple Multi-processing demonstration \n",
+    "\n",
+    "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK\n",
+    "\n",
+    "This note book uses SubprocVecEnv from SB3 for multi-processing."
    ]
   },
   {