Tennis_point5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collaboration and Competition\n",
    "\n",
    "---\n",
    "\n",
    "You are welcome to use this coding environment to train your agent for the project.  Follow the instructions below to get started!\n",
    "\n",
    "### 1. Start the Environment\n",
    "\n",
    "Run the next code cell to install a few packages.  This line will take a few minutes to run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mtensorflow 1.7.1 has requirement numpy>=1.13.3, but you'll have numpy 1.12.1 which is incompatible.\u001b[0m\r\n",
      "\u001b[31mipython 6.5.0 has requirement prompt-toolkit<2.0.0,>=1.0.15, but you'll have prompt-toolkit 3.0.16 which is incompatible.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip -q install ./python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The environment is already saved in the Workspace and can be accessed at the file path provided below. "
   ]
  },
  {
   "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"
     ]
    }
   ],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np\n",
    "\n",
    "env = UnityEnvironment(file_name=\"/data/Tennis_Linux_NoVis/Tennis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Environments contain **_brains_** which are responsible for deciding the actions of their associated agents. Here we check for the first brain available, and set it as the default brain we will be controlling from Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the default brain\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Examine the State and Action Spaces\n",
    "\n",
    "Run the code cell below to print some information about the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of agents: 2\n",
      "Size of each action: 2\n",
      "There are 2 agents. Each observes a state with length: 24\n",
      "The state for the first agent looks like: [ 0.          0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.          0.\n",
      "  0.          0.         -6.65278625 -1.5        -0.          0.\n",
      "  6.83172083  6.         -0.          0.        ]\n"
     ]
    }
   ],
   "source": [
    "# reset the environment\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "\n",
    "# number of agents \n",
    "num_agents = len(env_info.agents)\n",
    "print('Number of agents:', num_agents)\n",
    "\n",
    "# size of each action\n",
    "action_size = brain.vector_action_space_size\n",
    "print('Size of each action:', action_size)\n",
    "\n",
    "# examine the state space \n",
    "states = env_info.vector_observations\n",
    "state_size = states.shape[1]\n",
    "print('There are {} agents. Each observes a state with length: {}'.format(states.shape[0], state_size))\n",
    "print('The state for the first agent looks like:', states[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Take Random Actions in the Environment\n",
    "\n",
    "In the next code cell, you will learn how to use the Python API to control the agent and receive feedback from the environment.\n",
    "\n",
    "Note that **in this coding environment, you will not be able to watch the agents while they are training**, and you should set `train_mode=True` to restart the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnityEnvironmentException",
     "evalue": "No Unity environment is loaded.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnityEnvironmentException\u001b[0m                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-70f03db7c002>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m                                         \u001b[0;31m# play game for 5 episodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0menv_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbrain_name\u001b[0m\u001b[0;34m]\u001b[0m     \u001b[0;31m# reset the environment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mstates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv_info\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvector_observations\u001b[0m                  \u001b[0;31m# get the current state (for each agent)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_agents\u001b[0m\u001b[0;34m)\u001b[0m                          \u001b[0;31m# initialize the score (for each agent)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/unityagents/environment.py\u001b[0m in \u001b[0;36mreset\u001b[0;34m(self, train_mode, config, lesson)\u001b[0m\n\u001b[1;32m    270\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mUnityEnvironmentException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No Unity environment is loaded.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mvector_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAllBrainInfo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mUnityEnvironmentException\u001b[0m: No Unity environment is loaded."
     ]
    }
   ],
   "source": [
    "for i in range(5):                                         # play game for 5 episodes\n",
    "    env_info = env.reset(train_mode=False)[brain_name]     # reset the environment    \n",
    "    states = env_info.vector_observations                  # get the current state (for each agent)\n",
    "    scores = np.zeros(num_agents)                          # initialize the score (for each agent)\n",
    "    while True:\n",
    "        actions = np.random.randn(num_agents, action_size) # select an action (for each agent)\n",
    "        actions = np.clip(actions, -1, 1)                  # all actions between -1 and 1\n",
    "        env_info = env.step(actions)[brain_name]           # send all actions to tne environment\n",
    "        next_states = env_info.vector_observations         # get next state (for each agent)\n",
    "        rewards = env_info.rewards                         # get reward (for each agent)\n",
    "        dones = env_info.local_done                        # see if episode finished\n",
    "        scores += env_info.rewards                         # update the score (for each agent)\n",
    "        states = next_states                               # roll over states to next time step\n",
    "        if np.any(dones):                                  # exit loop if episode finished\n",
    "            break\n",
    "    print('Total score (averaged over agents) this episode: {}'.format(np.mean(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When finished, you can close the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. It's Your Turn!\n",
    "\n",
    "Now it's your turn to train your own agent to solve the environment!  A few **important notes**:\n",
    "- When training the environment, set `train_mode=True`, so that the line for resetting the environment looks like the following:\n",
    "```python\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "```\n",
    "- To structure your work, you're welcome to work directly in this Jupyter notebook, or you might like to start over with a new file!  You can see the list of files in the workspace by clicking on **_Jupyter_** in the top left corner of the notebook.\n",
    "- In this coding environment, you will not be able to watch the agents while they are training.  However, **_after training the agents_**, you can download the saved model weights to watch the agents on your own machine! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "from unityagents import UnityEnvironment\n",
    "from collections import deque\n",
    "from common import *\n",
    "import config\n",
    "from support import Experience, ReplayBuffer, OUNoise\n",
    "from agents import MultiDDPGAgent\n",
    "from workspace_utils import active_session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def step_tuple(env_info):\n",
    "    \"\"\" Returns a tuple of next state, reward, and done when the agent steps through the environment based\n",
    "        on the action taken\n",
    "        :param env_info: Object holding information about the environment at a certain point\n",
    "    \"\"\"\n",
    "    return env_info.vector_observations, env_info.rewards, env_info.local_done\n",
    "\n",
    "\n",
    "def ddpg(agent: MultiDDPGAgent, num_episodes=2000, target=0.5, max_time_steps=1000, saved_model=\"checkpoint.pth\"):\n",
    "    \"\"\" Train an agent using the DDPG algorithm\n",
    "\n",
    "        :param agent: a continuous control agent\n",
    "        :param num_episodes: the number of episodes to train the agent\n",
    "        :param target: The average target score the agent needs to achieve for optimal performance\n",
    "        :param max_time_steps: Maximum time steps per episode\n",
    "        :param saved_model: The file path to save the model weights\n",
    "    \"\"\"\n",
    "    now = datetime.datetime.now()\n",
    "    print(now, \"- Training a multi agent for max {} episodes. Target score to reach is {}\".format(num_episodes, target))\n",
    "    # collections to help keep track of the score\n",
    "    scores_deque = deque(maxlen=100)\n",
    "    scores = []\n",
    "    stats = {\"scores\": [], \"episodes\": []}  # collects stats for plotting purposes\n",
    "    mean_score = 0.\n",
    "\n",
    "    for episode in range(1, num_episodes + 1):\n",
    "        env_info = env.reset(train_mode=True)[brain_name]  # reset the environment\n",
    "        states = env_info.vector_observations\n",
    "        agent.reset()\n",
    "        score = np.zeros(num_agents)\n",
    "\n",
    "        for _ in range(max_time_steps):\n",
    "            actions = agent.act(states)\n",
    "            env_info = env.step(actions)[brain_name]\n",
    "            next_states, rewards, dones = step_tuple(env_info)\n",
    "            for idx in range(num_agents):\n",
    "                agent.step(Experience(states[idx], actions[idx], rewards[idx], next_states[idx], dones[idx]))\n",
    "            states = next_states\n",
    "            score += rewards\n",
    "            if np.any(dones):\n",
    "                break\n",
    "        max_score = max(score)\n",
    "        scores_deque.append(max_score)\n",
    "        scores.append(max_score)\n",
    "        mean_score = np.mean(scores_deque)\n",
    "\n",
    "        print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(episode, mean_score), end=\"\")\n",
    "        if episode % 100 == 0:\n",
    "            print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(episode, mean_score))\n",
    "\n",
    "        stats[\"scores\"].append(max_score)\n",
    "        stats[\"episodes\"].append(episode)\n",
    "\n",
    "        if mean_score >= target:\n",
    "            print('\\nEnvironment solved in {:d} episodes!\\tAverage Score: {:.2f}'.format(episode, mean_score))\n",
    "            print(\"Target score of {0} has been reached. Saving model to {1}\".format(target, saved_model))\n",
    "            torch.save(agent.local_actor_network().state_dict(), saved_model)\n",
    "            break\n",
    "\n",
    "    now = datetime.datetime.now()\n",
    "    print(now, \"- Finished training \" + \"successfully!\" if mean_score >= target else \"unsuccessfully!\")\n",
    "    return scores, stats\n",
    "\n",
    "\n",
    "def plot(stats):\n",
    "    scores = stats[\"scores\"]\n",
    "    episodes = stats[\"episodes\"]\n",
    "    plt.ylabel(\"Score\")\n",
    "    plt.xlabel(\"Episode #\")\n",
    "    plt.plot(episodes, scores)\n",
    "    fig = plt.figure()\n",
    "    fig.patch.set_facecolor('white')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialised 'Actor: Local' model\n",
      "Initialised 'Actor 0: Target' model\n",
      "Initialised 'Critic 0: Local' model\n",
      "Initialised 'Critic 0: Target' model\n",
      "Initialised 'Actor 1: Target' model\n",
      "Initialised 'Critic 1: Local' model\n",
      "Initialised 'Critic 1: Target' model\n",
      "2021-03-11 10:27:12.868193 - Training a multi agent for max 5000 episodes. Target score to reach is 0.5\n",
      "Episode 100\tAverage Score: 0.03\n",
      "Episode 200\tAverage Score: 0.04\n",
      "Episode 300\tAverage Score: 0.03\n",
      "Episode 400\tAverage Score: 0.05\n",
      "Episode 500\tAverage Score: 0.06\n",
      "Episode 600\tAverage Score: 0.16\n",
      "Episode 700\tAverage Score: 0.31\n",
      "Episode 800\tAverage Score: 0.38\n",
      "Episode 853\tAverage Score: 0.50\n",
      "Environment solved in 853 episodes!\tAverage Score: 0.50\n",
      "Target score of 0.5 has been reached. Saving model to checkpoint.pth\n",
      "2021-03-11 11:15:23.778114 - Finished training successfully!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd80c1b54a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd809e246a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "memory = ReplayBuffer(action_size, config.BUFFER_SIZE, config.BATCH_SIZE, random_seed=0)\n",
    "noise = OUNoise(action_size, 0)\n",
    "multi_agent = MultiDDPGAgent(state_size, action_size, num_agents, noise, memory)\n",
    "with active_session():\n",
    "    scores, stats = ddpg(agent=multi_agent, num_episodes=5000, target=0.5)\n",
    "    plot(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}