random.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random agent\n",
    "---\n",
    "This notebooks shows the behavior of two random agent in the tennis environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the tennis environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:unityagents:\n",
      "'Academy' started successfully!\n",
      "Unity Academy name: Academy\n",
      "        Number of Brains: 1\n",
      "        Number of External Brains : 1\n",
      "        Lesson number : 0\n",
      "        Reset Parameters :\n",
      "\t\t\n",
      "Unity brain name: TennisBrain\n",
      "        Number of Visual Observations (per agent): 0\n",
      "        Vector Observation space type: continuous\n",
      "        Vector Observation space size (per agent): 8\n",
      "        Number of stacked Vector Observation: 3\n",
      "        Vector Action space type: continuous\n",
      "        Vector Action space size (per agent): 2\n",
      "        Vector Action descriptions: , \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of agents: 2\n",
      "Action size: 2\n",
      "State size: 8\n"
     ]
    }
   ],
   "source": [
    "# start the environment\n",
    "env = UnityEnvironment(file_name=\"src/exec/Tennis.app\")\n",
    "# get default brain (responsible for deciding agent actions)\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]\n",
    "# examine state and action space\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "action_size = brain.vector_action_space_size\n",
    "state_size = brain.vector_observation_space_size\n",
    "n_agents = len(env_info.agents)\n",
    "print('Number of agents:', n_agents)\n",
    "print('Action size:', action_size)\n",
    "print('State size:', state_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Watch two random agents play for a few episodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 01\tScores  0.00 -0.01\n",
      "Episode 02\tScores  0.00  0.09\n",
      "Episode 03\tScores  0.00 -0.01\n",
      "Episode 04\tScores  0.00 -0.01\n",
      "Episode 05\tScores  0.10  0.19\n"
     ]
    }
   ],
   "source": [
    "for i_episode in range(1, 6):\n",
    "    env_info = env.reset(train_mode=False)[brain_name]\n",
    "    states = env_info.vector_observations\n",
    "    scores = np.zeros(n_agents)\n",
    "    while True:\n",
    "        actions = np.random.randn(n_agents, action_size)\n",
    "        actions = np.clip(actions, -1, 1)\n",
    "        env_info = env.step(actions)[brain_name]\n",
    "        next_states = env_info.vector_observations\n",
    "        rewards = env_info.rewards\n",
    "        dones = env_info.local_done\n",
    "        scores += env_info.rewards\n",
    "        states = next_states\n",
    "        if np.any(dones):\n",
    "            break\n",
    "    print('Episode {:02}\\tScores {:5.2f} {:5.2f}'.format(i_episode, scores[0], scores[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close the environment when done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  }
 ],
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