{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a Single agent system using RLLib\n",
    "\n",
    "© Crown-owned copyright 2025, Defence Science and Technology Laboratory UK\n",
    "\n",
    "This notebook demonstrates how to use the ``PrimaiteRayEnv`` to train a basic PPO agent on the [UC2 scenario](./Data-Manipulation-E2E-Demonstration.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!primaite setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "import ray\n",
    "from primaite.config.load import data_manipulation_config_path\n",
    "from primaite.session.ray_envs import PrimaiteRayEnv\n",
    "from ray.rllib.algorithms.ppo import PPOConfig\n",
    "\n",
    "# If you get an error saying this config file doesn't exist, you may need to run `primaite setup` in your command line\n",
    "# to copy the files to your user data path.\n",
    "with open(data_manipulation_config_path(), 'r') as f:\n",
    "    cfg = yaml.safe_load(f)\n",
    "\n",
    "ray.init(local_mode=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a Ray algorithm and pass it our config."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for agent in cfg['agents']:\n",
    "    if agent[\"ref\"] == \"defender\":\n",
    "        agent['agent_settings']['flatten_obs'] = True\n",
    "env_config = cfg\n",
    "\n",
    "config = (\n",
    "    PPOConfig()\n",
    "    .environment(env=PrimaiteRayEnv, env_config=env_config)\n",
    "    .env_runners(num_env_runners=0)\n",
    "    .training(train_batch_size=128)\n",
    "    .evaluation(evaluation_duration=1)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Start the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "algo = config.build()\n",
    "results = algo.train()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval = algo.evaluate()"
   ]
  }
 ],
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