PrimAITE/src/primaite/notebooks/training_example_ray_multi_agent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from primaite.game.game import PrimaiteGame\n",
    "import yaml\n",
    "from primaite.config.load import example_config_path\n",
    "\n",
    "from primaite.session.environment import PrimaiteRayEnv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(example_config_path(), 'r') as f:\n",
    "    cfg = yaml.safe_load(f)\n",
    "\n",
    "game = PrimaiteGame.from_config(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gym = PrimaiteRayEnv({\"game\":game})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ray\n",
    "from ray import air, tune\n",
    "from ray.rllib.algorithms.ppo import PPOConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ray.shutdown()\n",
    "ray.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from primaite.session.environment import PrimaiteRayMARLEnv\n",
    "\n",
    "\n",
    "env_config = {\"game\":game}\n",
    "config = (\n",
    "    PPOConfig()\n",
    "    .environment(env=PrimaiteRayMARLEnv, env_config={\"game\":game})\n",
    "    .rollouts(num_rollout_workers=0)\n",
    "    .multi_agent(\n",
    "        policies={agent.agent_name for agent in game.rl_agents},\n",
    "        policy_mapping_fn=lambda agent_id, episode, worker, **kw: agent_id,\n",
    "        )\n",
    "    .training(train_batch_size=128)\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tune.Tuner(\n",
    "    \"PPO\",\n",
    "    run_config=air.RunConfig(\n",
    "        stop={\"training_iteration\": 128},\n",
    "        checkpoint_config=air.CheckpointConfig(\n",
    "            checkpoint_frequency=10,\n",
    "        ),\n",
    "    ),\n",
    "    param_space=config\n",
    ").fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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