mlagentsenv.ipynb

  {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install mlagents\n",
    "%pip install mlagents_envs\n",
    "%pip install numpy\n",
    "\n",
    "#Run the below line and restart your kernel if there's a protobuf error\n",
    "%pip install --upgrade protobuf==3.19.0 \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy \n",
    "\n",
    "try:\n",
    "  import mlagents\n",
    "  print(\"ml-agents already installed\")\n",
    "except ImportError:\n",
    "  print(\"Something is messed up\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "environments closed\n"
     ]
    }
   ],
   "source": [
    "# Run this to make sure environments are closed\n",
    "\n",
    "try:\n",
    "  env.close()\n",
    "  print(\"environments closed\")\n",
    "except:\n",
    "  print(\"nothing closed\")\n",
    "  pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlagents_envs.envs import ThreeDBall  # or StrikersVsGoalie, Basic, ThreeDBall, (see docs)\n",
    "\n",
    "env = ThreeDBall.env()\n",
    "num_cycles = 1000\n",
    "\n",
    "env.reset()\n",
    "for agent in env.agent_iter(env.num_agents * num_cycles):\n",
    "    prev_observe, reward, done, info = env.last()\n",
    "    if isinstance(prev_observe, dict) and 'action_mask' in prev_observe:\n",
    "        action_mask = prev_observe['action_mask']\n",
    "    if done:\n",
    "        action = None\n",
    "    else:\n",
    "        action = env.action_spaces[agent].sample() # randomly choose an action for example\n",
    "    env.step(action)\n",
    "env.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlagents_envs.environment import UnityEnvironment\n",
    " \n",
    "env = UnityEnvironment(file_name=r\"\"\"\"C:\\Users\\friedmang\\Downloads\\dodgeball\\ml-agents-dodgeball-env-develop\\UnityEnvironment\"\"\", seed=1,  worker_id=3, side_channels=[]) #change to your pc\n",
    "env.reset()\n",
    "num_cycles = 1000\n",
    "\n",
    "for agent in env.agent_iter(env.num_agents * num_cycles):\n",
    "    prev_observe, reward, done, info = env.last()\n",
    "    if isinstance(prev_observe, dict) and 'action_mask' in prev_observe:\n",
    "        action_mask = prev_observe['action_mask']\n",
    "    if done:\n",
    "        action = None\n",
    "    else:\n",
    "        action = env.action_spaces[agent].sample() # randomly choose an action for example\n",
    "    env.step(action)\n",
    "exit()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.19"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}