IMAGINE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IVb1EQw89d7C"
   },
   "source": [
    "# <strong> Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration </strong>\n",
    "\n",
    "--------------------------------------------------------------------------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "awPzJKCs5dmE"
   },
   "source": [
    "This notebook is a walk-through the *IMAGINE* learning algorithm, from the [Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration](https://arxiv.org/pdf/2002.09253.pdf) paper.\n",
    "\n",
    "_We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning. Children do so by leveraging the compositionality of language as a tool to imagine descriptions of outcomes they never experienced before, targeting them as goals during play._\n",
    "\n",
    "#### <strong> The IMAGINE Setup </strong>\n",
    "\n",
    "The IMAGINE setup can be decomposed into two phases:\n",
    "\n",
    "* **Phase 1**: The agent starts with no prior knowledge on the set of possible interactions with its environment. It first explores randomly, then pursues its own goals as it discovers interesting interactions. A social partner (SP) provides linguistic descriptions of interesting interactions performed by the agent. These descriptions are turned into targetable goals by the agent. To represent goals, the agent trains jointly a language encoder that converts linguistic inputs to goal embeddings and a language-conditioned reward function.\n",
    "\n",
    "* **Phase 2**: The agent starts to imagine goals by composing known ones. It can train on them autonomously using learning signals from the internal reward function learned during Phase 1.\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?id=1Xre-pH-r5lXaT9kv33zTIOLrnpUelgNy\" width=\"600\"/>\n",
    "\n",
    "#### <strong> The IMAGINE Architecture </strong>\n",
    "\n",
    "1. The Goal Generator samples a target goal $g_{target}$ from the set of discovered and imagined goals ($\\mathcal{G}_d \\cup \\mathcal{G}_{im}$).\n",
    "2. The agent interacts with the environment (RL Agent) using its policy $\\pi$ conditioned by a goal embedding $g_{target}$.\n",
    "3. The state-action trajectories are stored in $mem(\\pi)$.\n",
    "4. SP observes the final state of the episode ($s_{T}$) and provides descriptions $\\mathcal{D}_{SP}(s_T)$ that the agent turns into targetable goals $\\mathcal{G}_{SP}(s_T)$.\n",
    "5. $mem(\\mathcal{R})$ stores positive pairs ($s_T,\\mathcal{G}_{SP}(s_T)$)and infersnegative pairs($s_T,\\mathcal{G}_\\text{na}(s_T)$).\n",
    "6.  The agent then updates:\n",
    "  - Goal Generator: Newly discovered goals are added to the set of known goals $\\mathcal{G_\\text{known}} \\leftarrow \\mathcal{G}_\\text{known} \\cup \\mathcal{G}_{SP}(s_T)$. If the agent is allowed to imagine new goals: $\\mathcal{G}_{im} \\leftarrow \\text{Imagination}(\\mathcal{G_\\text{known}})$.\n",
    "  - Language Encoder($L_e$) and Reward Function ($\\mathcal{R}$): updated using data from $mem(\\mathcal{R})$.\n",
    "  - RL agent(actor and critic):  A batch of state-action transitions $(s,a,s′)$ is sampled from $mem(\\pi)$.  Then Hindsight Replay and $\\mathcal{R}$ are used to select goals to train on and compute rewards $(s,a,s,g_{NL},r)$. Finally, the policy and critic are trained via RL.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HmGFArOeXvps"
   },
   "source": [
    "Configuring everything:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "Jo7scb9TnVcO",
    "outputId": "304576c4-9ef0-4f0f-9adf-8c742794bfa3"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import random\n",
    "import gym\n",
    "import pygame\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from IPython.display import HTML\n",
    "import os\n",
    "# os.environ['SDL_VIDEODRIVER'] = 'dummy'\n",
    "# os.environ['SDL_AUDIODRIVER'] = 'dummy'\n",
    "from src.imagine.experiment import config\n",
    "from src.imagine.goal_sampler import GoalSampler\n",
    "from src.utils.notebook_utils import get_params_for_notebook, get_modules_for_notebook, generate_animation_reward_module,\\\n",
    "generate_animation_policy_module, plot_tsne\n",
    "\n",
    "\n",
    "path= 'pretrained_weights/'\n",
    "params = get_params_for_notebook(path)\n",
    "policy_language_model, reward_language_model, policy, reward_function, goal_sampler = get_modules_for_notebook(path, params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <strong> 1. IMAGINE under the hood </strong>\n",
    "\n",
    "------------------------------------------------\n",
    "\n",
    "#### <strong> 1.1.1 First Role of the Social Partner: Setting Up The Scene </strong>\n",
    "\n",
    "\n",
    "The first role of the Social Partner (SP) is to set up the scene so that the goal targeted by the agent can be achieved.\n",
    "\n",
    "SP provides the agent with optimal learning opportunities by organizing the scene with:\n",
    "1. The required objects to reach the goal (not too difficult)\n",
    "2. Procedurally-generated distracting objects (not too easy and providing further discovery opportunities)\n",
    "\n",
    "The following lines of code show how scenes are generated according to this assumption:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enter a valid goal description\n",
    "goal_str = 'Grow blue cat' #Grow red cat, Grasp blue sofa, Grow green cow ...\n",
    "# Uncomment to sample a random goal from the training set\n",
    "# goal_str = np.random.choice(params['train_descriptions'])\n",
    "assert goal_str in params['train_descriptions'], 'Please enter a feasible goal description.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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5c+cyYMAAb/ArdXEeMpXqJIRAY2MjkyZNYtSoUSxZsoSFCxeycuXKzh6apL1whijVWQiB5uZmZs6cyQUXXMCZZ55JkvirJ3U1/lZKh1CfPn04/vjj33DBzRspFosHeUSSXmMQpUMsSRIaGhr2++tCCEydOrUOI5IEBlE65Pr06cOMGTP2++vGjh3L8OHD6zAiSWAQpUMuhMBJJ53EjBkzOnzodNy4ccydO5fGxsY6j07qvQyi1AkaGhp4xzvewcCBA/f5uf369ePcc8+lb9++h2BkUu/ltgupkxQKBc477zyq1eobfl6apsZQOgQMotRJQggMGTKks4ch6VUeMpUkCWeIklQ3bVuhs25qkqbQvx94xcCOM4iSVAdr18EnL0lZvqJz3n/gQPjqtRkTJ3TO+3dHBlGS6uCl1bBoMUDnTNHatsKixYEJR0dniR3kOURJknCGKEl119AQ+cpVOc39Y13fJ4+By65KWLN215TQ2WHHGURJqrM0heOmRBobYf36+rzHwAHQv3+kcf8vk6tXGURJOkQefjRwyefrc6bqw/8154Pvr+8MtKcziJJ0iMQIWV6fY5h5HgCD+OdwUY0kSRhESZIAgyhJEmAQJUkCDKIkSYBBlCQJMIiSJAEGUZIkwCBKkgQYREmSAIMoSRJgECVJAry4tyR1G0kSSV6dxlSrAN7s8GAyiJLUDTT1iXzu4pxpx9fuaPGNf034+f1gFA8egyhJ3cBfTI/MmR1J09rHV3whZ8WqlCW/69xx9SSeQ5SkbuDss3bFEKB/P2jq03nj6YkMoiR1Az+7L5Bluz5ub4dyufPG0xMZREnqBh59LPDT+YENG2DjJvjHrycsWtzZo+pZPIcoSd3AlrbAl65LaGysfbx1K8TogpqDySBKUjdRqQQqlc4eRc/lIVNJkjCIkiQBBlGSJMAgSpIEGERJkgCDKEkSYBAlSQIMoiRJgEGUJAkwiJIkAQZRkiTAIEqSBBhESaoT70TR3Xi3C0k6yGKEPN/1cXs7/PdPp2zdWr/3nHdn4KFHUlav2XMswTZ3iEGUpDr44wjleWDR8/V9v7XrAmvX1fc9ejoPmUrSQRYClEqRUqnzxpCmkcZGZ4f7wxmiJNXBpGPgqstyVq7snPcfMADOfHvsnDfvpgyiJNVBksDsMw1Sd+IhU0mSMIiSJAEGUZIkwCBKkgQYRO1DjJFyOZJlLg6Q1LMZRL2hGOHb366yZEktiFkWqVRq/4vRSErqOQyi9umJJ3KqVWhri3z961X+9m/LfOADZVavNoiSeg73IapDnnkm51/+JWPp0khLC6xdC9VqZ49Kkg4eZ4jqkB/9qFa/yy4rcuKJKePHB5qbO35NqA0bIgsWVHnooYxt25xZSup6nCGqQ9rb4UMfKjBsWOCGGzLOPjtlwICOfe327ZErrijz+99HqlWYNSvl4osLBC+yKKkLMYjaTbUad7tFTeHVn5CxYwNDhwauvrpCuQxnnZV2OGiVCixfHvnEJ4qsXJnz6KP5vr9Ikg4xgyigtr1i9erIvHkZd9+d7Xx87NjApk2RlSvh7/++zLZttcdvuKHKueemnHpqQpLsO4xpCscfn9DSgkGU1CUZRBFjZNWqyOWXV1i9OjJ9ekK/frXnVq2KbNpU+/eECQlDh9b+vWxZ5MtfrnDRRQXmzOn4bHHw4MDJJ3vqWlLXYxDFypWRK66o0NoaufzyItOnJzQ01AK3eXNk/fraIpjhwwN9+wZihI0bI9/8ZoWbbqottuloFMeOTRg71iBK6nr8y9TLZVnk9tszWlsjX/hCkZNPTnfGEKC5OTBuXMK4cQl9+9YeDwEGDQpcemmRE05I+O53q6xc6cpRSd2bQezl1q2LLFiQMWtWyowZ+/fjUCoFPvzhIv37Bx58MPfKNZK6NYPYy/3sZxnVKsyde2DbIFpa4KijAj/5SZVyuXY+ctOmyM9/Xpt1Zllk8+ZIntcOv1arkdbWyPz5VVpbvfybpK7Dc4i93NatMGFC2LmIZn+FEDjvvAKXX14G4JlnItdcU2bz5tph1VNPTbj77owdO+ATnygze3bKU0/lrF4d6dOnymc+U+TkkxP3JErqdM4QxeTJCf36HXiQSqXa/2/bBtdeW6FQCHzqU0UaGuCWWzLGjAlcckmRKVMSbrstY8CAwKc/XWTw4MD111d45ZWD9I1I0p/BGaIOmhihXI5cdVWJyZMTDjsMLr64whlnpJx5Zu3KNgsX5px/fsrMmQkTJhT5+MfL5G5LlNQFOEMUzz+fs3XrgZ/Le/1pwB07ag+8dsWb156vVnl1y0bt43L5gN9Skg46Z4hi0aJIWxv07XtgX3/PPRkxQpJAv36Ba6+tcMwxGc8+m5PncMcdGU88kbN+faRchm99q8IDDyT84Q+RQqF2FRtJ6mzOEHu5k05KaGz8815j2bJInz6Bpqba3TD69g089FDOxo3Q2AhLl0Z+85uc3/0u0thYmyH+5je146SXXVaipeUgfCOS9GcyiL3c0UcnhABPPZXt+5P3IssiWQbvfndKsQjjxgVuvLHEmDGBWbMSvvjFItOm1X7MJk4MXHllkXe9K2Xo0NrnHXdccIWppC7BIPZySQL9+weeeurAVrYsXhx5+umcYrEWthACixfnrFoVOeOMlBNPTPnc54oMHAif/WyRE05IOeuslHXrIg88kBlDSV2GQezlmprgjDMS1q3jgBbWVKswdGjglFN2/SgddlhtX2OW1Tbqr18fqVRqh1ZjjGzYECmVahf6lqSuwkU1vVySBEaOTLj55gpr1kTGjet4pGKM3H57lebm3RfkjBoVmDw54cYbq/z7v8PatbV7LH796xVuuSXw8suRSZMSZs70v8ckdR3+RRKnn54wcGDtMm77Y9u22o1/3/nOlD8+8hlC4KKLiowaFVi2LNLaCgMHwpYt8OKLkdGjEy69tEih4AxRUtdhEEWawmmnpTzySM66dR07bBpj5Ne/zmhvhxkz9rz1U0sLfOxjRZqbA29/e8J115V417tSmprgox8tMHhwPb4TSTpwBlGkaWD27JQtWyJLl3bsrhU7dsB//EfG4YcHhg3bc6YXQqCtLbJuXWTq1IQJExLe+taUHTugtTW6mEZSl+M5xF6sra12V4o8Z+fG+htvrHLOOfveKb9kSc7KlZHjjku47bbqXj9n/fraloxHH80pl6v84Q+RbdvggQdyli2rRTdJYNaslKYmAympc4X9uf3OlClT4rx58+o4HB1KixfnXHRRmezAtiAeFKUSfP/7DQwdahAlHRrjx49/IsY44/WPO0PsxcaODbz3vSk335xx2GGB97wnZdKk+h9Ff/zxnB/8oEq1CuefX2DQoLq/pSTtk0HsxUqlwPvfX6CxMXDXXRnf+16Vz3++yMyZ9bk/YYyRhx7K+fGPqwwdWovxrFkpaersUFLnc1FNL1coBC64IOWGG2r3J7zmmgq33ZaxadPBu5t9jJHW1shNN1X5ylcqDB8euO66ErNnG0NJXYczRBFCYMQIuPLKIldeWeHb365y770Zb3pTwsGYKOZ57TDpSy9FxowJXHFFkWHDcKWppC7FIAqoxWnkyMA3vlHi/vsz7rgj4/bbD95qm/HjA5/8ZIHTT3dFqaSuySBqN01NgTlzCpx2Wsq2bQfzdaG52RBK6roMovaquTnQ3NzZo5CkQ8dFNZIkYRAlSQIMoiRJgEGUJAkwiJIkAQZRkiTAIEqSBBhESZIAgyhJEmAQJUkCDKIkSYBBlCQJMIiSJAEGUZIkwCBKkgQYREmSAIMoSRJgECVJAgyiJEmAQZQkCTCIkiQBBlGSJMAgSpIEGERJkgCDKEkSYBAlSQIMoiRJgEGUJAkwiJIkAQZRkiTAIEqSBBhESZIAgyhJEmAQJUkCDKIkSYBBlCQJMIiSJAEGUZIkwCBKkgQYREmSAIMoSRJgECVJAgyiJEmAQZQkCTCIkiQBBlGSJMAgSpIEGERJkgCDKEkSYBAlSQIMoiRJgEGUJAkwiJIkAQZRkiTAIEqSBBhESZIAgyhJEmAQJUkCDKIkSYBBlCQJMIiSJAEGUZIkwCBKkgQYREmSAIMoSRJgECVJAgyiJEmAQZQkCTCIkiQBBlGSJMAgSpIEGERJkgCDKEkSYBAlSQIMoiRJgEGUJAkwiJIkAQZRkiTAIEqSBBhESZIAgyhJEmAQJUkCDKIkSYBBlCQJMIiSJAEGUZIkwCBKkgQYREmSAIMoSRJgECVJAgyiJEmAQZQkCTCIkiQBBlGSJMAgSpIEGERJkgCDKEkSYBAlSQIMoiRJgEGUJAkwiJIkAQZRkiTAIEqSBBhESZIAgyhJEmAQJUkCDKIkSYBBlCQJMIiSJAEGUZIkwCBKkgQYREmSAIMoSRJgECVJAgyiJEkAhBhjxz85hPXAsvoNR5KkuhsdYxzy+gf3K4iSJPVUHjKVJAmDKEkSYBAlSQIMoiRJgEGUJAkwiJIkAQZRkiTAIEqSBBhESZIA+P8lkzuFnsgewAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a scene which is compatible with the goal description\n",
    "env = gym.make(params['conditions']['env_name'], display=False)\n",
    "env.reset()\n",
    "initial_o = env.unwrapped.reset_with_goal(goal_str)\n",
    "env.step(np.array([0, 0, 0]))\n",
    "env.render(close=True)\n",
    "obs = pygame.surfarray.array3d(env.viewer).transpose([1,0,2])\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "fig = plt.imshow(obs)\n",
    "fig.axes.get_xaxis().set_visible(False)\n",
    "fig.axes.get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "214I7YWy9h98"
   },
   "source": [
    "We observe that the generated scene contains the object specified in the goal description as well as two distractor objects.\n",
    "\n",
    "Note that if your 'goal_str' implies growing an animal, the social partner also takes care of setting up with either food or water to grow it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3LHg1vnYEIbK"
   },
   "source": [
    "#### <strong> 1.1.2 Second Role of the Social Partner: Providing Descriptions</strong>\n",
    "\n",
    "\n",
    "\n",
    "The second role of the social partner is to provide language descriptions about the goals reached by the agent __at the end of an episode__.\n",
    "\n",
    "In this section, we play a policy conditioned by the goal we defined in 1.1.1 and observe the descriptive feedback given by the social partner at the end of the episode.\n",
    "\n",
    "We first do the rollout of the policy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "32QSAzXwWj1z"
   },
   "outputs": [],
   "source": [
    "def generate_animated_rollout(env, goal_str, initial_o, policy, policy_language_model, reset=False):\n",
    "    # In order to build vizualization\n",
    "    fig = plt.figure()\n",
    "    ims = []\n",
    "\n",
    "    ## Rollout of a policy\n",
    "    if reset:\n",
    "        env.reset()\n",
    "        initial_o = env.unwrapped.reset_with_goal(goal_str)\n",
    "        o = initial_o.copy()\n",
    "    else:\n",
    "        o = initial_o.copy()\n",
    "    goal_encoding = policy_language_model.encode(goal_str)\n",
    "    episode={'obs':[]}\n",
    "    for t in range(30):\n",
    "        action = policy.get_actions(o, goal_encoding)\n",
    "        o,_,_,_ = env.step(action)\n",
    "        episode['obs'].append(o)\n",
    "        env.render(close=True)\n",
    "        obs = pygame.surfarray.array3d(env.viewer).transpose([1,0,2])\n",
    "        plt.rcParams['figure.figsize'] = [12, 8]\n",
    "        im = plt.imshow(obs, animated=True)\n",
    "        ims.append([im])\n",
    "      \n",
    "    ani = animation.ArtistAnimation(fig, ims, interval=400, blit=False,\n",
    "                                  repeat=False)\n",
    "    return ani, episode, o, obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "ani, episode, o, obs = generate_animated_rollout(env, goal_str, initial_o, policy, policy_language_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "NTguMjkuMTAw\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(ani.to_html5_video())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Exhaustive feedback:__\n",
    "\n",
    "In its default configuration, the social partner gives __exhaustive feedbacks__: it outputs all the goals that the agent has achieved at the end of the episode. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Descriptive Feedback from Social Partner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Go left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Go bottom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Go bottom left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grasp blue living_thing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grasp blue cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grasp any blue thing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Grasp any living_thing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Grasp any cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Grow blue animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Grow blue cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Grow any blue thing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Grow any animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Grow any cat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Descriptive Feedback from Social Partner\n",
       "0                                   Go left\n",
       "1                                 Go bottom\n",
       "2                            Go bottom left\n",
       "3                   Grasp blue living_thing\n",
       "4                            Grasp blue cat\n",
       "5                      Grasp any blue thing\n",
       "6                    Grasp any living_thing\n",
       "7                             Grasp any cat\n",
       "8                          Grow blue animal\n",
       "9                             Grow blue cat\n",
       "10                      Grow any blue thing\n",
       "11                          Grow any animal\n",
       "12                             Grow any cat"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.imagine.social_partner import SocialPartner\n",
    "oracle_reward_function = config.get_oracle_reward_function(goal_sampler, params)\n",
    "social_partner = SocialPartner(oracle_reward_function=oracle_reward_function,\n",
    "                               goal_sampler=goal_sampler,\n",
    "                               feedback_strategy='exhaustive',\n",
    "                               p_availability=1.,\n",
    "                               params=params)\n",
    "exhaustive_feedback = social_partner.get_feedback([episode])[0][0]\n",
    "pd.DataFrame(exhaustive_feedback, columns=['Descriptive Feedback from Social Partner'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The agent can then infer the negative feedbacks by contrasting the set of goals already discovered and the set of goals achieved in the episode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Negative examples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Grasp red food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Grasp red rose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grow green bonsai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grow blue window</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grow any sofa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Go center right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Grasp any carnivorous</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Negative examples\n",
       "0         Grasp red food\n",
       "1         Grasp red rose\n",
       "2      Grow green bonsai\n",
       "3       Grow blue window\n",
       "4          Grow any sofa\n",
       "5        Go center right\n",
       "6  Grasp any carnivorous"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_examples = set(goal_sampler.feedback_memory['string']) - set(exhaustive_feedback)\n",
    "negative_examples = pd.DataFrame(negative_examples, columns=['Negative examples'])\n",
    "negative_examples.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__One positive example and one negative one__:\n",
    "\n",
    "In our paper, we relax the exhaustive feedback configuration and train our system with a social partner giving only one positive and one negative feedback at the end of each episode. \n",
    "\n",
    "Here is an example of how the social partner operates in that configuration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 531
    },
    "id": "WeVvEj0wbU8I",
    "outputId": "560e50ba-1c9b-4a98-cf34-b4ff15f00261"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Positive feedback</th>\n",
       "      <th>Negative feedback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Grow blue animal</td>\n",
       "      <td>Grasp red lamp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Positive feedback Negative feedback\n",
       "0  Grow blue animal    Grasp red lamp"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_partner2 = SocialPartner(oracle_reward_function=oracle_reward_function,\n",
    "                                goal_sampler=goal_sampler,\n",
    "                                feedback_strategy='one_pos_one_neg',\n",
    "                                p_availability=1.,\n",
    "                                params=params)\n",
    "\n",
    "pos, neg = social_partner2.get_one_pos_one_neg_feedback([episode])\n",
    "\n",
    "# one_pos_one_neg_df = \n",
    "pd.DataFrame({'Positive feedback': pos, 'Negative feedback':neg })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the generated scene contains the object of the specified goal description as well as two distractor objects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6UsYaxIE4jrw"
   },
   "source": [
    "### <strong> 1.2 The Reward Function Module </strong>\n",
    "\n",
    "------------\n",
    "\n",
    "#### <strong> 1.2.1 The architecture explained </strong>\n",
    "\n",
    "The Reward Function is implemented using a [Deep Set](https://arxiv.org/abs/1703.06114) architecture. \n",
    "\n",
    "[Deep Set](https://arxiv.org/abs/1703.06114) is a network architecture implementing set functions (input permutation invariance). Each  input  is  mapped  separately  to  some latent space using a shared network. These latent representations are then passed through a permutation-invariant function (e.g. mean, sum) to ensure the permutation-invariance of the whole function. In the case of our reward function, inputs are grouped into object-dependent sub-states $s_{obj_i}$, each mapped to a __object level reward__ by a same network $NN_{reward}$ (weight sharing). $NN_{reward}$ can be thought of as a single-object reward function which estimates whether object $i$ verifies the goal ($R_{obj_i} >0.5$) or not.   Object level rewards are then nmapped  into  a  global  binary  reward  using  a  logical OR function: _if any object verifies the goal,  then the wholescene verifies it_.  \n",
    "\n",
    "In addition to object-dependent inputs, the computation of $R_{obj_i}$ integrates goal information through a [gated-attention mechanism](https://arxiv.org/abs/1706.07230). Instead of being concatenated, the goal embeddingg is cast into an attention vector  $\\alpha$ before being combined to the object-dependentsub-state through an Hadamard product (term-by-term) to form the inputs of $NN_{reward}$. _This can beseen  as  scaling  object-specific  features  according  to  the interpretation of the goal $g_{NL}$_.  \n",
    "\n",
    "The small diagram below gives an illustration of the reward function architecture:\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?export=view&id=1-74Gx9PkoPz4DC2gNPkdlSDeQsRVCq5G\" alt=\"drawing\" width=\"850\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <strong> 1.2.2 Vizualizing the object level rewards for two different goals</strong>\n",
    "\n",
    "In the following blocks of code we will compute the object specific reward for two different goals:\n",
    "\n",
    "1. the achieved goal\n",
    "2. another goal that is not achieved but that involve one of the distractor\n",
    "\n",
    "Let's consider the following state (the final state from our previous rollout):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.imshow(obs)\n",
    "fig.axes.get_xaxis().set_visible(False)\n",
    "fig.axes.get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define two functions. The first one uses the reward function in inference mode in order to compute the probability of reward for each object in the scene. The second is used to generate a random goal which imply the object in the scene. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_reward_probabilities(state, goal_str, reward_language_model, reward_function, env):\n",
    "    goal_encoding = reward_language_model.encode(goal_str)\n",
    "    input_o = torch.tensor(state).float().view(1,len(o))\n",
    "    input_goal = torch.tensor(goal_encoding).float().view(1,len(goal_encoding))\n",
    "    reward_per_object = reward_function.reward_function.compute_logits_before_or(input_o, input_goal)\n",
    "    reward_per_object = [elem.detach()[0][0].item() for elem in reward_per_object]\n",
    "    objects = [obj.object_descr['colors'] + ' ' + obj.object_descr['types'] for obj in env.objects]\n",
    "    d = {'Objects':objects, 'Reward':reward_per_object}\n",
    "    df_rew = pd.DataFrame(d)\n",
    "    return df_rew\n",
    "\n",
    "def generate_other_possible_goal(goal_str, env):\n",
    "    objects = [obj.object_descr['colors'] + ' ' + obj.object_descr['types'] for obj in env.objects]\n",
    "    other_objects = [o for o in objects if o not in goal_str]\n",
    "    return ' '.join(random.sample(['Grow'], 1) + random.sample(other_objects, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We recall the achieved goal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Grow blue cat'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goal_str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The non-achieved goal involving a distractor object is the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grow blue door\n"
     ]
    }
   ],
   "source": [
    "other_goal_str = generate_other_possible_goal(goal_str,env)\n",
    "print(other_goal_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now plot the object level rewards for these two goals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8eec213a20>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_rew1 = compute_reward_probabilities(o, goal_str, reward_language_model, reward_function, env)\n",
    "df_rew2 = compute_reward_probabilities(o, other_goal_str, reward_language_model, reward_function, env)\n",
    "\n",
    "f, axes = plt.subplots(1, 2, figsize=(16, 7),sharey=True)\n",
    "sns.despine(left=True)\n",
    "sns.barplot(x=\"Objects\", y=\"Reward\", data=df_rew1, ax=axes[0]).set_title(goal_str)\n",
    "for index, row in df_rew1.iterrows():\n",
    "    axes[0].text(row.name, row.Reward + 0.03, '{:.2f}'.format(row.Reward), color='black', ha=\"center\")\n",
    "axes[0].plot([-0.5, 2.6], [0.5, 0.5], linestyle='--', color='k')\n",
    "\n",
    "sns.despine(left=True)\n",
    "sns.barplot(x=\"Objects\", y=\"Reward\", data=df_rew2, ax=axes[1]).set_title(other_goal_str)\n",
    "for index, row in df_rew2.iterrows():\n",
    "    axes[1].text(row.name, row.Reward + 0.03, '{:.2f}'.format(row.Reward), color='black', ha=\"center\")\n",
    "axes[1].plot([-0.5, 2.6], [0.5, 0.5], linestyle='--', color='k')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our deepset architecture operating at the object level is confidently detecting the goal achievement (left): $R_{object}>0.5$ (above dashed line).\n",
    "\n",
    "For the goal that is not fulfilled (right), the architecture correctly predicts that there is no reward for the three objects. However, we can notice that the model outputs a higher signal for the object mentioned in the goal description. This means that the reward function is able to understand which object is used in the goal description without outputting a reward signal > 0.5 if the goal involving this object is not achieved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KK5Cfu0ColeQ"
   },
   "source": [
    "#### <strong> 1.2.3 Visualizing the evolution of the object level rewards during rollout </strong>\n",
    "\n",
    "In this small section we simply look at the evolution of the object level rewards during a rollout. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "Es80gPecozDr"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "ani = generate_animation_reward_module(env, goal_str, reward_language_model, reward_function, policy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "MA==\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(ani.to_html5_video())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <strong> 1.3 The Policy Module </strong>\n",
    "------------\n",
    "\n",
    "#### <strong> 1.3.1 The architecture explained </strong>\n",
    "\n",
    "The actor and critic architectures are both goal conditioned:\n",
    "$a=\\pi(s,g_{NL})$ and $value=Q(s,a,g_{NL})$.\n",
    "\n",
    "They follow the same implementation as the reward function. Instead of casting the object features to a scalar like we do for the reward function, the shared-network of the actor and critic casts $s_{obj_i}$ into a high dimensional space. We then use the sum as permutation-invariant function in order to predict the action for the actor and the value for the critic.\n",
    "\n",
    "The diagram below shows the policy's architecture next to the reward function one. \n",
    "\n",
    "Note that the actor and critic are both goal conditioned. However, we do not backpropagate the gradient throught the language encoder during training. The language encoding is only performed by the reward function.\n",
    "\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?export=view&id=1zcpO_WtCCQxDpkSbnoRkIwUQM29_tWxC\" alt=\"drawing\" width=\"1000\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <strong> 1.3.2 Visualizing the norm of the latent vector for each object </strong>\n",
    "\n",
    "In a similar fashion as we did for the reward function, we propose to look at the norm of the latent vector for each object ($|z_(x_i,g)|$ in the diagram on top) during inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "OPjdOPCNpvz-"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "ani = generate_animation_policy_module(env, goal_str, reward_language_model, policy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(ani.to_html5_video())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5p-Lt8Bh1yGp"
   },
   "source": [
    "### <strong> 1.4 The Goal Imagination Module </strong>\n",
    "\n",
    "-------------\n",
    "\n",
    "Here is the goal imagination module inspired by Construction Grammar. For each pair of known goals that differ by exactly one word, the two differing words are considered equivalent. This means they can be exchanged in any known goal to form a novel goal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "T_-ihcP72E9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weird\n",
      "Percentage of the test set found: 0.875\n",
      "Percentage of the new descriptions that are not in the test 0.5333333333333333\n",
      "Percentage of the new descriptions that are in the test set 0.4666666666666667\n",
      "  Examples of sentences in the generated set, not in the test:\n",
      "0                                  Grow green carpet          \n",
      "1                                  Grow green supply          \n",
      "2                                    Grow blue table          \n",
      "3                                     Grow any chair          \n",
      "4                                      Grow any food          \n",
      "5                                Grow green cupboard          \n",
      "6                                   Grow blue window          \n",
      "  Examples of sentences in the test set, found by generation:\n",
      "0                                    Grasp green dog         \n",
      "1                                    Grow blue algae         \n",
      "2                                   Grow green plant         \n",
      "3                                  Grow green bonsai         \n",
      "4                                     Grow red algae         \n",
      "5                                    Grasp green fly         \n",
      "6                               Grow any carnivorous         \n",
      "  Examples of sentences in the test set, , not in the generated set:\n",
      "0                                 Grasp green flower                \n",
      "1                                  Grasp blue flower                \n",
      "2                                   Grasp any flower                \n",
      "3                                   Grow blue flower                \n",
      "4                                   Grasp red flower                \n",
      "5                                    Grow red flower                \n",
      "6                                    Grow any flower                \n"
     ]
    }
   ],
   "source": [
    "from src.imagine.goal_generator.simple_sentence_generator import SentenceGeneratorHeuristic\n",
    "generator = SentenceGeneratorHeuristic(train_descriptions=params['train_descriptions'],\n",
    "                                       test_descriptions=params['test_descriptions'],\n",
    "                                       sentences=None, \n",
    "                                       method='CGH')\n",
    "# update the set of known goals\n",
    "generator.update_model(params['train_descriptions'])\n",
    "# generate imagined goals\n",
    "new_descriptions = generator.generate_sentences()\n",
    "\n",
    "p_found_in_test = sum([d in params['test_descriptions'] for d in new_descriptions]) / len(params['test_descriptions'])\n",
    "p_not_in_test = sum([d not in params['test_descriptions'] for d in new_descriptions]) / len(new_descriptions)\n",
    "p_in_test = sum([d in params['test_descriptions'] for d in new_descriptions]) / len(new_descriptions)\n",
    "print('Percentage of the test set found:', p_found_in_test)\n",
    "print('Percentage of the new descriptions that are not in the test', p_not_in_test)\n",
    "print('Percentage of the new descriptions that are in the test set', p_in_test)\n",
    "\n",
    "data = pd.DataFrame({'Examples of sentences in the generated set, not in the test:': list( set(new_descriptions) - set(params['test_descriptions'])) })\n",
    "print(data.head(7))\n",
    "\n",
    "data = pd.DataFrame({'Examples of sentences in the test set, found by generation:': list(set(new_descriptions).intersection(set(params['test_descriptions']))) })\n",
    "print(data.head(7))\n",
    "\n",
    "data = pd.DataFrame({'Examples of sentences in the test set, , not in the generated set:': list(set(params['test_descriptions']) - set(new_descriptions)) })\n",
    "print(data.head(7))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JCTxPAQd2JO_"
   },
   "source": [
    "## <strong> 2. How to train IMAGINE <strong>\n",
    "\n",
    "------------\n",
    "    \n",
    "In this small section we simply recall the command line that should be used to train all modules of the IMAGINE system in order to replicate results from our paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "H7oGU_JD2Ko-"
   },
   "outputs": [],
   "source": [
    "# !python train.py --num_cpu=6 --architecture=modular_attention --imagination_method=CGH --reward_function=learned_lstm  --goal_invention=from_epoch_10 --n_epochs=167"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T3njuS192W5V"
   },
   "source": [
    "## <strong> 3. Examples of Generalization </strong>\n",
    "\n",
    "----------------\n",
    "\n",
    "We now propose to vizualise rollouts of policies conditioned on testing goals.\n",
    "\n",
    "The Venn Diagram below shows the distribution of training and testing goals. We see that all testing goals cannot be generated by our imagination heurisitc based on constructions grammar. The testing goals that are missing are the sentences containing the 'flower' token. Those goals cannot be generated by the goal imagination module because the 'flower' token is never seen by the agent (never communicated by SP).\n",
    "\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?export=view&id=1l1eHKvLIdZlvKRZQhqs9Oj4Gq42xQqWw\" alt=\"drawing\" width=\"500\"/>\n",
    "\n",
    "The list of goals from $\\mathcal{G}_\\text{test}$ can be obtained as follow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "8l8oiTCC2ZxC"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test goals never communicated by the social partner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Grasp any animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Grasp any flower</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grasp any fly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grasp blue animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grasp blue door</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Grow red living_thing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Grow red plant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Grow red rose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Grow red tea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Grow red tree</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Test goals never communicated by the social partner\n",
       "0                                    Grasp any animal \n",
       "1                                    Grasp any flower \n",
       "2                                       Grasp any fly \n",
       "3                                   Grasp blue animal \n",
       "4                                     Grasp blue door \n",
       "..                                                ... \n",
       "59                              Grow red living_thing \n",
       "60                                     Grow red plant \n",
       "61                                      Grow red rose \n",
       "62                                       Grow red tea \n",
       "63                                      Grow red tree \n",
       "\n",
       "[64 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(params['test_descriptions'],columns=['Test goals never communicated by the social partner'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among the testing descriptions are goals that involve growing plants. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "7kZ1_h2J2bOO"
   },
   "outputs": [],
   "source": [
    "# Enter goal here from g_test\n",
    "g_test = 'Grow blue tea'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "ani, _, _, _ = generate_animated_rollout(env, g_test, [], policy, policy_language_model, reset=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "wABoQAAAABgBnhlqQS8AAAMAAAMAAZtzLJUAAAMAKSEAAAAfQZodSahBbJlMCCX//rUqgAAAAwAA\n",
       "AwAAAwAAAwB6wAAAABlBnjtFFSwS/wAAAwAAAwABm2j2ioAAABSRAAAAGAGeXGpBLwAAAwAAAwAB\n",
       "m3MslQAAAwApIQAABH9tb292AAAAbG12aGQAAAAAAAAAAAAAAAAAAAPoAAAu4AABAAABAAAAAAAA\n",
       "AAAAAAAAAQAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
       "AAAAAAAAAAAAAAACAAADqXRyYWsAAABcdGtoZAAAAAMAAAAAAAAAAAAAAAEAAAAAAAAu4AAAAAAA\n",
       "AAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAEAAAAADYAAAAkAAAAAA\n",
       "ACRlZHRzAAAAHGVsc3QAAAAAAAAAAQAALuAAACAAAAEAAAAAAyFtZGlhAAAAIG1kaGQAAAAAAAAA\n",
       "AAAAAAAAACgAAAHgAFXEAAAAAAAtaGRscgAAAAAAAAAAdmlkZQAAAAAAAAAAAAAAAFZpZGVvSGFu\n",
       "ZGxlcgAAAALMbWluZgAAABR2bWhkAAAAAQAAAAAAAAAAAAAAJGRpbmYAAAAcZHJlZgAAAAAAAAAB\n",
       "AAAADHVybCAAAAABAAACjHN0YmwAAAC0c3RzZAAAAAAAAAABAAAApGF2YzEAAAAAAAAAAQAAAAAA\n",
       "AAAAAAAAAAAAAAADYAJAAEgAAABIAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
       "AAAAAAAY//8AAAAyYXZjQwFkAB//4QAZZ2QAH6zZQNgSaEAAAAMAgAAAAwKDxgxlgAEABmjr48si\n",
       "wAAAABx1dWlka2hA8l8kT8W6OaUbzwMj8wAAAAAAAAAYc3R0cwAAAAAAAAABAAAAHgAAEAAAAAAU\n",
       "c3RzcwAAAAAAAAABAAAAAQAAAOhjdHRzAAAAAAAAABsAAAABAAAgAAAAAAEAADAAAAAAAQAAEAAA\n",
       "AAACAAAgAAAAAAEAAEAAAAAAAgAAEAAAAAABAAAgAAAAAAEAAFAAAAAAAQAAIAAAAAABAAAAAAAA\n",
       "AAEAABAAAAAAAQAAMAAAAAABAAAQAAAAAAEAAFAAAAAAAQAAIAAAAAABAAAAAAAAAAEAABAAAAAA\n",
       "AQAAUAAAAAABAAAgAAAAAAEAAAAAAAAAAQAAEAAAAAABAABQAAAAAAEAACAAAAAAAQAAAAAAAAAB\n",
       "AAAQAAAAAAEAAEAAAAAAAgAAEAAAAAAcc3RzYwAAAAAAAAABAAAAAQAAAB4AAAABAAAAjHN0c3oA\n",
       "AAAAAAAAAAAAAB4AABskAAAAuAAAAK0AAADRAAAAwwAAAiwAAACyAAAAcgAAANUAAAI9AAAA7QAA\n",
       "AHgAAAB5AAACewAAAM4AAASQAAABewAAAJ4AAADXAAAAKQAAAB0AAAAcAAAAHAAAAYAAAAAwAAAA\n",
       "LAAAABwAAAAjAAAAHQAAABwAAAAUc3RjbwAAAAAAAAABAAAALAAAAGJ1ZHRhAAAAWm1ldGEAAAAA\n",
       "AAAAIWhkbHIAAAAAAAAAAG1kaXJhcHBsAAAAAAAAAAAAAAAALWlsc3QAAAAlqXRvbwAAAB1kYXRh\n",
       "AAAAAQAAAABMYXZmNTguMjkuMTAw\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(ani.to_html5_video())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GdxUhwT82iOX"
   },
   "source": [
    "## 4.  Vizualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "LdmD7YIw2jaI"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.utils.notebook_utils import plot_attention_vector\n",
    "goal_encoding = torch.tensor(reward_language_model.encode(goal_str))\n",
    "attention_vector = reward_function.reward_function.get_attention_vector(goal_encoding).detach().numpy()\n",
    "plot_attention_vector(attention_vector, goal_str, params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize the representations of goals learned by the reward functions via a T-SNE projection of the goal embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "aXrlM4Gg2nQY"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from sklearn.manifold import TSNE\n",
    "from ipywidgets import interact\n",
    "code = 'categories'\n",
    " \n",
    "embs = {}\n",
    "for descr in params['train_descriptions']+params['test_descriptions']:\n",
    "    goal_encoding = reward_language_model.encode(descr)\n",
    "    embs[descr]=goal_encoding\n",
    "\n",
    "descr = sorted(list(embs.keys()))\n",
    "embeddings = np.array([embs[d] for d in descr])\n",
    "X_embedded = TSNE(n_components=2,\n",
    "                  perplexity=20,\n",
    "                  n_iter=5000,\n",
    "                  n_iter_without_progress=1000,\n",
    "                  learning_rate=10).fit_transform(embeddings)\n",
    "plot_tsne(X_embedded, descr, code, params)\n"
   ]
  }
 ],
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