Continuous_Control.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Continuous Control\n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, you will learn how to use the Unity ML-Agents environment for the second project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) program.\n",
    "\n",
    "### 1. Start the Environment\n",
    "\n",
    "We begin by importing the necessary packages.  If the code cell below returns an error, please revisit the project instructions to double-check that you have installed [Unity ML-Agents](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Installation.md) and [NumPy](http://www.numpy.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will start the environment!  **_Before running the code cell below_**, change the `file_name` parameter to match the location of the Unity environment that you downloaded.\n",
    "\n",
    "- **Mac**: `\"path/to/Reacher.app\"`\n",
    "- **Windows** (x86): `\"path/to/Reacher_Windows_x86/Reacher.exe\"`\n",
    "- **Windows** (x86_64): `\"path/to/Reacher_Windows_x86_64/Reacher.exe\"`\n",
    "- **Linux** (x86): `\"path/to/Reacher_Linux/Reacher.x86\"`\n",
    "- **Linux** (x86_64): `\"path/to/Reacher_Linux/Reacher.x86_64\"`\n",
    "- **Linux** (x86, headless): `\"path/to/Reacher_Linux_NoVis/Reacher.x86\"`\n",
    "- **Linux** (x86_64, headless): `\"path/to/Reacher_Linux_NoVis/Reacher.x86_64\"`\n",
    "\n",
    "For instance, if you are using a Mac, then you downloaded `Reacher.app`.  If this file is in the same folder as the notebook, then the line below should appear as follows:\n",
    "```\n",
    "env = UnityEnvironment(file_name=\"Reacher.app\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found path: /home/minhna4lab/minhna1112/study/RL/deep-reinforcement-learning/p2_continuous-control/./Reacher_Linux_NoVis/Reacher.x86_64\n",
      "Mono path[0] = '/home/minhna4lab/minhna1112/study/RL/deep-reinforcement-learning/p2_continuous-control/./Reacher_Linux_NoVis/Reacher_Data/Managed'\n",
      "Mono config path = '/home/minhna4lab/minhna1112/study/RL/deep-reinforcement-learning/p2_continuous-control/./Reacher_Linux_NoVis/Reacher_Data/MonoBleedingEdge/etc'\n",
      "Preloaded 'libgrpc_csharp_ext.x64.so'\n",
      "Unable to preload the following plugins:\n",
      "\tlibgrpc_csharp_ext.x86.so\n",
      "Logging to /home/minhna4lab/.config/unity3d/Unity Technologies/Unity Environment/Player.log\n"
     ]
    },
    {
     "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\tgoal_size -> 5.0\n",
      "\t\tgoal_speed -> 1.0\n",
      "Unity brain name: ReacherBrain\n",
      "        Number of Visual Observations (per agent): 0\n",
      "        Vector Observation space type: continuous\n",
      "        Vector Observation space size (per agent): 33\n",
      "        Number of stacked Vector Observation: 1\n",
      "        Vector Action space type: continuous\n",
      "        Vector Action space size (per agent): 4\n",
      "        Vector Action descriptions: , , , \n"
     ]
    }
   ],
   "source": [
    "env = UnityEnvironment(file_name='./Reacher_Linux_NoVis/Reacher.x86_64')"
   ]
  },
  {
   "attachments": {},
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the default brain\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Examine the State and Action Spaces\n",
    "\n",
    "In this environment, a double-jointed arm can move to target locations. A reward of `+0.1` is provided for each step that the agent's hand is in the goal location. Thus, the goal of your agent is to maintain its position at the target location for as many time steps as possible.\n",
    "\n",
    "The observation space consists of `33` variables corresponding to position, rotation, velocity, and angular velocities of the arm.  Each action is a vector with four numbers, corresponding to torque applicable to two joints.  Every entry in the action vector must be a number between `-1` and `1`.\n",
    "\n",
    "Run the code cell below to print some information about the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of agents: 1\n",
      "Size of each action: 4\n",
      "There are 1 agents. Each observes a state with length: 33\n",
      "The state for the first agent looks like: [ 0.00000000e+00 -4.00000000e+00  0.00000000e+00  1.00000000e+00\n",
      " -0.00000000e+00 -0.00000000e+00 -4.37113883e-08  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00 -1.00000000e+01  0.00000000e+00\n",
      "  1.00000000e+00 -0.00000000e+00 -0.00000000e+00 -4.37113883e-08\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  5.75471878e+00 -1.00000000e+00\n",
      "  5.55726671e+00  0.00000000e+00  1.00000000e+00  0.00000000e+00\n",
      " -1.68164849e-01]\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])"
   ]
  },
  {
   "attachments": {},
   "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",
    "Once this cell is executed, you will watch the agent's performance, if it selects an action at random with each time step.  A window should pop up that allows you to observe the agent, as it moves through the environment.  \n",
    "\n",
    "Of course, as part of the project, you'll have to change the code so that the agent is able to use its experience to gradually choose better actions when interacting with the environment!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 33)\n",
      "Total score (averaged over agents) this episode: 0.0\n"
     ]
    }
   ],
   "source": [
    "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",
    "    \n",
    "print('Total score (averaged over agents) this episode: {}'.format(np.mean(scores)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When finished, you can close the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# env.close()"
   ]
  },
  {
   "attachments": {},
   "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!  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",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "import matplotlib.pyplot as plt\n",
    "from agent.ddpg import Agent\n",
    "agent = Agent(state_size=33, action_size=4, random_seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 100\tAverage Score: 0.24\n",
      "Episode 200\tAverage Score: 0.43\n",
      "Episode 300\tAverage Score: 0.36\n",
      "Episode 400\tAverage Score: 0.27\n",
      "Episode 500\tAverage Score: 0.31\n",
      "Episode 600\tAverage Score: 0.27\n",
      "Episode 700\tAverage Score: 0.26\n",
      "Episode 800\tAverage Score: 0.29\n",
      "Episode 900\tAverage Score: 0.15\n",
      "Episode 1000\tAverage Score: 0.19\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "def ddpg(n_episodes=1000, max_t=300, print_every=100):\n",
    "    scores_deque = deque(maxlen=print_every)\n",
    "    scores = []\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "        env_info = env.reset(train_mode=True)[brain_name] \n",
    "        # states = np.reshape(env_info.vector_observations, newshape=[agent.state_size, -1])  \n",
    "        state = env_info.vector_observations[0]\n",
    "        agent.reset()\n",
    "        score = 0\n",
    "        for t in range(max_t):\n",
    "            # print(\"Size of states returned from the environment\", states.shape)\n",
    "            action = agent.act(state)\n",
    "            # next_state, reward, done, _ = env.step(action)\n",
    "            env_info = env.step(action)[brain_name]\n",
    "            next_state = env_info.vector_observations[0]         # get next state (for each agent)\n",
    "            done = env_info.local_done[0]\n",
    "            reward = env_info.rewards[0]                         # get reward (for each agent)\n",
    "            agent.step(state, action, reward, next_state, done)\n",
    "            state = next_state \n",
    "            score += reward\n",
    "            # update the score (for each agent)\n",
    "            # see if episode finished\n",
    "            if done:\n",
    "                break \n",
    "        scores_deque.append(score)\n",
    "        scores.append(score)\n",
    "        print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)), end=\"\")\n",
    "        torch.save(agent.actor_local.state_dict(), 'checkpoint_actor.pth')\n",
    "        torch.save(agent.critic_local.state_dict(), 'checkpoint_critic.pth')\n",
    "        if i_episode % print_every == 0:\n",
    "            print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)))\n",
    "            \n",
    "    return scores\n",
    "\n",
    "scores = ddpg()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(np.arange(1, len(scores)+1), scores)\n",
    "plt.ylabel('Score')\n",
    "plt.xlabel('Episode #')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 100\tAverage Score: 0.69\tScore: 0.00\n",
      "Episode 200\tAverage Score: 0.28\tScore: 0.00\n",
      "Episode 300\tAverage Score: 0.47\tScore: 0.00\n",
      "Episode 400\tAverage Score: 0.82\tScore: 0.72\n",
      "Episode 500\tAverage Score: 0.58\tScore: 0.15\n",
      "Episode 600\tAverage Score: 0.93\tScore: 2.62\n",
      "Episode 700\tAverage Score: 1.03\tScore: 1.67\n",
      "Episode 800\tAverage Score: 0.99\tScore: 0.02\n",
      "Episode 900\tAverage Score: 0.05\tScore: 0.08\n",
      "Episode 1000\tAverage Score: 0.06\tScore: 0.18\n",
      "Episode 1100\tAverage Score: 0.06\tScore: 0.00\n",
      "Episode 1200\tAverage Score: 0.04\tScore: 0.00\n",
      "Episode 1300\tAverage Score: 0.05\tScore: 0.00\n",
      "Episode 1400\tAverage Score: 0.04\tScore: 0.00\n",
      "Episode 1500\tAverage Score: 0.05\tScore: 0.00\n",
      "Episode 1600\tAverage Score: 0.05\tScore: 0.00\n",
      "Episode 1700\tAverage Score: 0.04\tScore: 0.00\n",
      "Episode 1800\tAverage Score: 0.05\tScore: 0.00\n",
      "Episode 1900\tAverage Score: 0.05\tScore: 0.00\n",
      "Episode 2000\tAverage Score: 0.04\tScore: 0.14\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def ddpg(n_episodes=2000, max_t=10000):\n",
    "    scores_deque = deque(maxlen=100)\n",
    "    scores = []\n",
    "    max_score = -np.Inf\n",
    "    i_episode = 1\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "    #while True:\n",
    "        # reset the environment\n",
    "        env_info = env.reset(train_mode=True)[brain_name]    \n",
    "        state = env_info.vector_observations                  # get the current state (for each agent)\n",
    "        agent.reset()\n",
    "        score = 0\n",
    "        for t in range(max_t):\n",
    "            action = agent.act(state)\n",
    "            env_info = env.step(action)[brain_name]           # send all actions to tne environment\n",
    "            next_state = env_info.vector_observations         # get next state (for each agent)\n",
    "            reward = env_info.rewards[0]                         # get reward (for each agent)\n",
    "            done = env_info.local_done[0]                        # see if episode finished\n",
    "            agent.step(state, action, reward, next_state, done)\n",
    "            state = next_state\n",
    "            score += reward\n",
    "            if done:\n",
    "                break \n",
    "        scores_deque.append(score)\n",
    "        scores.append(score)\n",
    "        print('\\rEpisode {}\\tAverage Score: {:.2f}\\tScore: {:.2f}'.format(i_episode, np.mean(scores_deque), score), end=\"\")\n",
    "        if i_episode % 100 == 0:\n",
    "            print('\\rEpisode {}\\tAverage Score: {:.2f}\\tScore: {:.2f}'.format(i_episode, np.mean(scores_deque), score))\n",
    "        if np.mean(scores_deque) >= 30.:\n",
    "            torch.save(agent.actor_local.state_dict(), 'checkpoint_actor.pth')\n",
    "            torch.save(agent.critic_local.state_dict(), 'checkpoint_critic.pth')\n",
    "            print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)))\n",
    "            break\n",
    "    return scores\n",
    "\n",
    "scores = ddpg()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(np.arange(len(scores)), scores)\n",
    "plt.ylabel('Score')\n",
    "plt.xlabel('Episode #')\n",
    "fig.savefig('result.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ddpg(n_episodes=2000, max_t=10000):\n",
    "    scores_deque = deque(maxlen=100)\n",
    "    scores = []\n",
    "    max_score = -np.Inf\n",
    "    i_episode = 1\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "    #while True:\n",
    "        # reset the environment\n",
    "        env_info = env.reset(train_mode=True)[brain_name]    \n",
    "        state = env_info.vector_observations                  # get the current state (for each agent)\n",
    "        agent.reset()\n",
    "        score = 0\n",
    "        for t in range(max_t):\n",
    "            action = agent.act(state)\n",
    "            env_info = env.step(action)[brain_name]           # send all actions to tne environment\n",
    "            next_state = env_info.vector_observations         # get next state (for each agent)\n",
    "            reward = env_info.rewards[0]                         # get reward (for each agent)\n",
    "            done = env_info.local_done[0]                        # see if episode finished\n",
    "            agent.step(state, action, reward, next_state, done)\n",
    "            state = next_state\n",
    "            score += reward\n",
    "            if done:\n",
    "                break \n",
    "        scores_deque.append(score)\n",
    "        scores.append(score)\n",
    "        print('\\rEpisode {}\\tAverage Score: {:.2f}\\tScore: {:.2f}'.format(i_episode, np.mean(scores_deque), score), end=\"\")\n",
    "        if i_episode % 100 == 0:\n",
    "            print('\\rEpisode {}\\tAverage Score: {:.2f}\\tScore: {:.2f}'.format(i_episode, np.mean(scores_deque), score))\n",
    "        if np.mean(scores_deque) >= 30.:\n",
    "            torch.save(agent.actor_local.state_dict(), 'checkpoint_actor.pth')\n",
    "            torch.save(agent.critic_local.state_dict(), 'checkpoint_critic.pth')\n",
    "            print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)))\n",
    "            break\n",
    "    return scores\n",
    "\n",
    "scores = ddpg()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(np.arange(len(scores)), scores)\n",
    "plt.ylabel('Score')\n",
    "plt.xlabel('Episode #')\n",
    "fig.savefig('result.png')"
   ]
  }
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