Rename notebooks

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

@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train a Multi agent system using RLLIB\n",
+    "\n",
+    "This notebook will demonstrate how to use the `PrimaiteRayMARLEnv` to train a very basic system with two PPO agents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### First, Import packages and read our config file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from primaite.game.game import PrimaiteGame\n",
+    "import yaml\n",
+    "\n",
+    "from primaite.session.environment import PrimaiteRayEnv\n",
+    "from primaite import PRIMAITE_PATHS\n",
+    "\n",
+    "import ray\n",
+    "from ray import air, tune\n",
+    "from ray.rllib.algorithms.ppo import PPOConfig\n",
+    "from primaite.session.environment import PrimaiteRayMARLEnv\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(PRIMAITE_PATHS.user_config_path / 'example_config/data_manipulation_marl.yaml', 'r') as f:\n",
+    "    cfg = yaml.safe_load(f)\n",
+    "\n",
+    "ray.init(local_mode=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a Ray algorithm config which accepts our two agents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = (\n",
+    "    PPOConfig()\n",
+    "    .multi_agent(\n",
+    "        policies={'defender_1','defender_2'}, # These names are the same as the agents defined in the example config.\n",
+    "        policy_mapping_fn=lambda agent_id, episode, worker, **kw: agent_id,\n",
+    "        )\n",
+    "    .environment(env=PrimaiteRayMARLEnv, env_config=cfg)#, disable_env_checking=True)\n",
+    "    .rollouts(num_rollout_workers=0)\n",
+    "    .training(train_batch_size=128)\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Set training parameters and start the training\n",
+    "This example will save outputs to a default Ray directory and use mostly default settings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tune.Tuner(\n",
+    "    \"PPO\",\n",
+    "    run_config=air.RunConfig(\n",
+    "        stop={\"timesteps_total\": 512},\n",
+    "    ),\n",
+    "    param_space=config\n",
+    ").fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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
+}