{
 "cells": [
  {
   "cell_type": "markdown",
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "from primaite.config.load import data_manipulation_config_path\n",
    "\n",
    "from primaite.session.ray_envs import PrimaiteRayEnv\n",
    "import ray\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",
    ")\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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
