#2453 - Committing additional explanations to notebook

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

@@ -6,9 +6,14 @@
    "source": [
     "# Training an SB3 Agent\n",
     "\n",
-    "This notebook will demonstrate how to use primaite to create and train a PPO agent.\n",
-    "\n",
-    "#### First, import `PrimaiteGymEnv` and read our config file"
+    "This notebook will demonstrate how to use primaite to create and train a PPO agent, using a pre-defined configuration file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### First, we import the inital packages and read in our configuration file."
    ]
   },
   {
@@ -38,7 +43,14 @@
    "outputs": [],
    "source": [
     "with open(data_manipulation_config_path(), 'r') as f:\n",
-    "    cfg = yaml.safe_load(f)\n"
+    "    cfg = yaml.safe_load(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the given configuration, we generate the environment our agent will train in."
    ]
   },
   {
@@ -50,6 +62,13 @@
     "gym = PrimaiteGymEnv(game_config=cfg)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets define training parameters for the agent."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -71,7 +90,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = PPO('MlpPolicy', gym, learning_rate=LEARNING_RATE,  n_steps=NO_STEPS, batch_size=BATCH_SIZE, verbose=0, tensorboard_log=\"./PPO_UC2/\")\n"
+    "model = PPO('MlpPolicy', gym, learning_rate=LEARNING_RATE,  n_steps=NO_STEPS, batch_size=BATCH_SIZE, verbose=0, tensorboard_log=\"./PPO_UC2/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the agent configured, let's train for our defined number of episodes."
    ]
   },
   {
@@ -80,8 +106,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.learn(total_timesteps=NO_STEPS)\n",
-    "model.save(\"PrimAITE-PPO-example-agent\")"
+    "model.learn(total_timesteps=NO_STEPS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, let's save the agent to a zip file that can be used in future evaluation."
    ]
   },
   {
@@ -93,6 +125,13 @@
     "model.save(\"PrimAITE-PPO-example-agent\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we load the saved agent and run it in evaluation mode."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -103,6 +142,13 @@
     "eval_model = PPO.load(\"PrimAITE-PPO-example-agent\", gym)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, evaluate the agent."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,