{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple multi-processing demonstration\n",
    "\n",
    "© Crown-owned copyright 2024, Defence Science and Technology Laboratory UK\n",
    "\n",
    "This notebook uses SubprocVecEnv from SB3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import packages and read config file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "from stable_baselines3 import PPO\n",
    "from stable_baselines3.common.utils import set_random_seed\n",
    "from stable_baselines3.common.vec_env import SubprocVecEnv\n",
    "\n",
    "from primaite.session.environment import PrimaiteGymEnv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from primaite.config.load import data_manipulation_config_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(data_manipulation_config_path(), 'r') as f:\n",
    "    cfg = yaml.safe_load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "EPISODE_LEN = 128\n",
    "NUM_EPISODES = 10\n",
    "NO_STEPS = EPISODE_LEN * NUM_EPISODES\n",
    "BATCH_SIZE = 32\n",
    "LEARNING_RATE = 3e-4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define an environment function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def make_env(rank: int, seed: int = 0) -> callable:\n",
    "    \"\"\"Wrapper script for _init function.\"\"\"\n",
    "\n",
    "    def _init() -> PrimaiteGymEnv:\n",
    "        env = PrimaiteGymEnv(env_config=cfg)\n",
    "        env.reset(seed=seed + rank)\n",
    "        model = PPO(\n",
    "            \"MlpPolicy\",\n",
    "            env,\n",
    "            learning_rate=LEARNING_RATE,\n",
    "            n_steps=NO_STEPS,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            verbose=0,\n",
    "            tensorboard_log=\"./PPO_UC2/\",\n",
    "        )\n",
    "        model.learn(total_timesteps=NO_STEPS)\n",
    "        return env\n",
    "\n",
    "    set_random_seed(seed)\n",
    "    return _init\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_procs = 2\n",
    "train_env = SubprocVecEnv([make_env(i + n_procs) for i in range(n_procs)])\n"
   ]
  }
 ],
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