Modeling_Cognitive_Tasks.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Azi_bLWn6mAR"
      },
      "source": [
        "# Using RL to Model Cognitive Tasks\n",
        "\n",
        "**By Neurmatch Academy**\n",
        "\n",
        "__Content creators:__ Morteza Ansarinia, Yamil Vidal\n",
        "\n",
        "__Production editor:__ Spiros Chavlis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t0JJoEeC6mAb"
      },
      "source": [
        "<p align='center'><img src='https://github.com/NeuromatchAcademy/widgets/blob/master/sponsors.png?raw=True'/></p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HbuVnWO6mAc"
      },
      "source": [
        "---\n",
        "# Objective\n",
        "\n",
        "- This project aims to use behavioral data to train an agent and then use the agent to investigate data produced by human subjects. Having a computational agent that mimics humans in such tests, we will be able to compare its mechanics with human data.\n",
        "\n",
        "- In another conception, we could fit an agent that learns many cognitive tasks that require abstract-level constructs such as executive functions. This is a multi-task control problem.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-hzSA9mE6mAd"
      },
      "source": [
        "---\n",
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zigNT_hI6mAd",
        "outputId": "4dbae952-22e3-40f7-d6b5-6fb1512e7a54"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |█                               | 10 kB 27.9 MB/s eta 0:00:01\r\u001b[K     |██                              | 20 kB 31.3 MB/s eta 0:00:01\r\u001b[K     |███▏                            | 30 kB 33.2 MB/s eta 0:00:01\r\u001b[K     |████▏                           | 40 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 51 kB 13.1 MB/s eta 0:00:01\r\u001b[K     |██████▎                         | 61 kB 15.1 MB/s eta 0:00:01\r\u001b[K     |███████▎                        | 71 kB 15.0 MB/s eta 0:00:01\r\u001b[K     |████████▍                       | 81 kB 14.1 MB/s eta 0:00:01\r\u001b[K     |█████████▍                      | 92 kB 15.4 MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 102 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |███████████▌                    | 112 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |████████████▌                   | 122 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |█████████████▋                  | 133 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |██████████████▋                 | 143 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 153 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 163 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████▊              | 174 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████▉             | 184 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████▉            | 194 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 204 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 215 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 225 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 235 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 245 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 256 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▏    | 266 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▏   | 276 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▏  | 286 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▎ | 296 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 307 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 314 kB 15.7 MB/s \n",
            "\u001b[33mWARNING: dm-acme 0.4.0 does not provide the extra 'tensorflow'\u001b[0m\n",
            "\u001b[K     |████████████████████████████████| 3.8 MB 39.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 72 kB 431 kB/s \n",
            "\u001b[K     |████████████████████████████████| 342 kB 9.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 202 kB 41.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 145 kB 57.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 117 kB 53.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 46.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 4.0 MB 43.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 668.3 MB 18 kB/s \n",
            "\u001b[K     |████████████████████████████████| 5.7 MB 57.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 462 kB 68.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 596 kB 60.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 217 kB 75.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 9.1 MB 66.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 51 kB 9.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 272 kB 71.0 MB/s \n",
            "\u001b[?25h  Building wheel for dm-acme (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 88 kB 8.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 18.8 MB 157 kB/s \n",
            "\u001b[K     |████████████████████████████████| 207 kB 68.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 4.9 MB 62.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 99 kB 10.7 MB/s \n",
            "\u001b[?25h  Building wheel for bsuite (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 254 kB 30.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 104 kB 33.6 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# @title Install dependencies\n",
        "# !pip install --upgrade pip setuptools wheel --quiet\n",
        "!pip install dm-acme[jax,tensorflow] --quiet\n",
        "!pip install dm-acme[envs] --quiet\n",
        "!pip install dm-sonnet --quiet\n",
        "!pip install trfl --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "IOdC2K-56mAk"
      },
      "outputs": [],
      "source": [
        "# Imports\n",
        "\n",
        "import time\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sonnet as snt\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import dm_env\n",
        "\n",
        "import acme\n",
        "from acme import specs\n",
        "from acme import wrappers\n",
        "from acme import EnvironmentLoop\n",
        "from acme.agents.tf import dqn\n",
        "from acme.utils import loggers\n",
        "from acme.agents.tf import r2d2\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "VIvUNa5t6mAl"
      },
      "outputs": [],
      "source": [
        "# @title Figure settings\n",
        "from IPython.display import clear_output, display, HTML\n",
        "%matplotlib inline\n",
        "sns.set()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-R0grYI-6mAm"
      },
      "source": [
        "---\n",
        "# Background\n",
        "\n",
        "- Cognitive scientists use standard lab tests to tap into specific processes in the brain and behavior. Some examples of those tests are Stroop, N-back, Digit Span, TMT (Trail making tests), and WCST (Wisconsin Card Sorting Tests).\n",
        "\n",
        "- Despite an extensive body of research that explains human performance using descriptive what-models, we still need a more sophisticated approach to gain a better understanding of the underlying processes (i.e., a how-model).\n",
        "\n",
        "- Interestingly, many of such tests can be thought of as a continuous stream of stimuli and corresponding actions, that is in consonant with the RL formulation. In fact, RL itself is in part motivated by how the brain enables goal-directed behaviors using reward systems, making it a good choice to explain human performance.\n",
        "\n",
        "- One behavioral test example would be the N-back task.\n",
        "\n",
        "  - In the N-back, participants view a sequence of stimuli, one by one, and are asked to categorize each stimulus as being either match or non-match. Stimuli are usually numbers, and feedback is given at both timestep and trajectory levels.\n",
        "\n",
        "  - The agent is rewarded when its response matches the stimulus that was shown N steps back in the episode. A simpler version of the N-back uses two-choice action schema, that is match vs non-match. Once the present stimulus matches the one presented N step back, then the agent is expected to respond to it as being a `match`.\n",
        "\n",
        "\n",
        "- Given a trained RL agent, we then find correlates of its fitted parameters with the brain mechanisms. The most straightforward composition could be the correlation of model parameters with the brain activities."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jr2eTAmn6mAo"
      },
      "source": [
        "## Datasets\n",
        "\n",
        "- HCP WM task ([NMA-CN HCP notebooks](https://github.com/NeuromatchAcademy/course-content/tree/master/projects/fMRI))\n",
        "\n",
        "Any dataset that used cognitive tests would work.\n",
        "Question: limit to behavioral data vs fMRI?\n",
        "Question: Which stimuli and actions to use?\n",
        "classic tests can be modeled using 1) bounded symbolic stimuli/actions (e.g., A, B, C), but more sophisticated one would require texts or images (e.g., face vs neutral images in social stroop dataset)\n",
        "The HCP dataset from NMA-CN contains behavioral and imaging data for 7 cognitive tests including various versions of N-back."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hi23aJh46mAp"
      },
      "source": [
        "## N-back task\n",
        "\n",
        "In the N-back task, participants view a sequence of stimuli, one per time, and are asked to categorize each stimulus as being either match or non-match. Stimuli are usually numbers, and feedbacks are given at both timestep and trajectory levels.\n",
        "\n",
        "In a typical neuro setup, both accuracy and response time are measured, but here, for the sake of brevity, we focus only on accuracy of responses."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BCw1SqR6mAq"
      },
      "source": [
        "---\n",
        "# Cognitive Tests Environment\n",
        "\n",
        "First we develop an environment in that agents perform a cognitive test, here the N-back."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H7aDZqad6mAr"
      },
      "source": [
        "## Human dataset\n",
        "\n",
        "We need a dataset of human perfoming a N-back test, with the following features:\n",
        "\n",
        "- `participant_id`: following the BIDS format, it contains a unique identifier for each participant.\n",
        "- `trial_index`: same as `time_step`.\n",
        "- `stimulus`: same as `observation`.\n",
        "- `response`: same as `action`, recorded response by the human subject.\n",
        "- `expected_response`: correct response.\n",
        "- `is_correct`: same as `reward`, whether the human subject responded correctly.\n",
        "- `response_time`: won't be used here.\n",
        "\n",
        "Here we generate a mock dataset with those features, but remember to **replace this with real human data.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvgbdQuK6mAs"
      },
      "source": [
        "### Environment\n",
        "\n",
        "The following cell implments N-back envinronment, that we later use to train a RL agent on human data. It is capable of performing two kinds of simulation:\n",
        "- rewards the agent once the action was correct (i.e., a normative model of the environment).\n",
        "- receives human data (or mock data if you prefer), and returns what participants performed as the observation. This is more useful for preference-based RL."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ufg4-znW6mAs"
      },
      "outputs": [],
      "source": [
        "class NBack(dm_env.Environment):\n",
        "\n",
        "  ACTIONS = ['match', 'non-match']\n",
        "\n",
        "  def __init__(self,\n",
        "               N=2,\n",
        "               episode_steps=32,\n",
        "               stimuli_choices=list('ABCDEF'),\n",
        "               human_data=None,\n",
        "               seed=1,\n",
        "               prob = None\n",
        "               ):\n",
        "    \"\"\"\n",
        "    Args:\n",
        "      N: Number of steps to look back for the matched stimuli. Defaults to 2 (as in 2-back).\n",
        "      episode_steps\n",
        "      stimuli_choices\n",
        "      human_data\n",
        "      seed\n",
        "\n",
        "    \"\"\"\n",
        "    self.N = N\n",
        "    self.episode_steps = episode_steps\n",
        "    self.stimuli_choices = stimuli_choices\n",
        "    self.stimuli = np.empty(shape=episode_steps)  # will be filled in the `reset()`\n",
        "    self.n_s = len(self.stimuli_choices)\n",
        "    if prob is None:\n",
        "      self.prob = 0.5 * (self.n_s - 2)/ (self.n_s - 1)\n",
        "    else:\n",
        "      self.prob = prob\n",
        "    self._reset_next_step = True\n",
        "\n",
        "    # whether mimic humans or reward the agent once it responds optimally.\n",
        "    if human_data is None:\n",
        "      self._imitate_human = False\n",
        "      self.human_data = None\n",
        "      self.human_subject_data = None\n",
        "    else:\n",
        "      self._imitate_human = True\n",
        "      self.human_data = human_data\n",
        "      self.human_subject_data = None\n",
        "\n",
        "    self._action_history = []\n",
        "\n",
        "  def reset(self):\n",
        "    self._reset_next_step = False\n",
        "    self._current_step = 0\n",
        "    self._action_history.clear()\n",
        "\n",
        "    # generate a random sequence instead of relying on human data\n",
        "    if self.human_data is None:\n",
        "      for i in range(self.episode_steps):\n",
        "          if i<self.N:\n",
        "            self.stimuli[i]=np.random.choice(self.n_s)\n",
        "          else:\n",
        "            rand = np.random.random()\n",
        "            if rand < self.prob:\n",
        "              self.stimuli[i]=self.stimuli[i-self.N]\n",
        "            else:\n",
        "              self.stimuli[i]=np.random.choice(self.n_s)\n",
        "    else:\n",
        "      self.human_subject_data = self.human_data.query('participant_id == participant_id.sample().iloc[0]',\n",
        "                                                engine='python').sort_values('trial_index')\n",
        "      self.stimuli = self.human_subject_data['stimulus'].to_list()\n",
        "      self.stimuli = np.array([ord(s) - ord('A') + 1 for s in self.stimuli]).astype(np.float32)\n",
        "\n",
        "    return dm_env.restart(self._observation())\n",
        "\n",
        "\n",
        "  def _episode_return(self):\n",
        "    if self._imitate_human:\n",
        "      return np.mean(self.human_subject_data['response'] == self._action_history)\n",
        "    else:\n",
        "      return 0.0\n",
        "\n",
        "  def step(self, action: int):\n",
        "    if self._reset_next_step:\n",
        "      return self.reset()\n",
        "\n",
        "    agent_action = NBack.ACTIONS[action]\n",
        "\n",
        "    if self._imitate_human:\n",
        "      human_action = self.human_subject_data['response'].iloc[self._current_step]\n",
        "      step_reward = 0. if (agent_action == human_action) else -1.\n",
        "    else:\n",
        "      expected_action = 'match' if (self.stimuli[self._current_step] == self.stimuli[self._current_step - self.N]) else 'non-match'\n",
        "      step_reward = 0. if (agent_action == expected_action) else -1.\n",
        "\n",
        "    self._action_history.append(agent_action)\n",
        "\n",
        "    self._current_step += 1\n",
        "\n",
        "    if self._current_step == self.stimuli.shape[0]:\n",
        "      self._reset_next_step = True\n",
        "      return dm_env.termination(reward=self._episode_return(),\n",
        "                                observation=self._observation())\n",
        "    else:\n",
        "      return dm_env.transition(reward=step_reward,\n",
        "                               observation=self._observation())\n",
        "\n",
        "  def observation_spec(self):\n",
        "    return dm_env.specs.BoundedArray(\n",
        "        shape=self.stimuli.shape,\n",
        "        dtype=self.stimuli.dtype,\n",
        "        name='nback_stimuli', minimum=0, maximum=len(self.stimuli_choices) + 1)\n",
        "\n",
        "  def action_spec(self):\n",
        "    return dm_env.specs.DiscreteArray(\n",
        "        num_values=len(NBack.ACTIONS),\n",
        "        dtype=np.int32,\n",
        "        name='action')\n",
        "\n",
        "  def _observation(self):\n",
        "    obs = self.stimuli[:self._current_step+1].copy()\n",
        "    obs = np.pad(obs,(0, len(self.stimuli) - len(obs)))\n",
        "\n",
        "    return obs\n",
        "\n",
        "  def plot_state(self):\n",
        "    \"\"\"Display current state of the environment.\n",
        "\n",
        "     Note: `M` mean `match`, and `.` is a `non-match`.\n",
        "    \"\"\"\n",
        "    stimuli = self.stimuli[:self._current_step - 1]\n",
        "    actions = ['M' if a=='match' else '.' for a in self._action_history[:self._current_step - 1]]\n",
        "    return HTML(\n",
        "        f'<b>Environment ({self.N}-back):</b><br />'\n",
        "        f'<pre><b>Stimuli:</b> {\"\".join(map(str,map(int,stimuli)))}</pre>'\n",
        "        f'<pre><b>Actions:</b> {\"\".join(actions)}</pre>'\n",
        "    )\n",
        "\n",
        "  @staticmethod\n",
        "  def create_environment(prob = None):\n",
        "    \"\"\"Utility function to create a N-back environment and its spec.\"\"\"\n",
        "\n",
        "    # Make sure the environment outputs single-precision floats.\n",
        "    environment = wrappers.SinglePrecisionWrapper(NBack(prob = prob))\n",
        "\n",
        "    # Grab the spec of the environment.\n",
        "    environment_spec = specs.make_environment_spec(environment)\n",
        "\n",
        "    return environment, environment_spec"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQTnbj5V6mAt"
      },
      "source": [
        "### Define a random agent\n",
        "\n",
        "For more information you can refer to NMA-DL W3D2 Basic Reinforcement learning."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "_h1bWf8D6mAu"
      },
      "outputs": [],
      "source": [
        "class RandomAgent(acme.Actor):\n",
        "\n",
        "  def __init__(self, environment_spec, action = None):\n",
        "    \"\"\"Gets the number of available actions from the environment spec.\"\"\"\n",
        "    self._num_actions = environment_spec.actions.num_values\n",
        "    self.action = action\n",
        "\n",
        "  def select_action(self, observation):\n",
        "    \"\"\"Selects an action uniformly at random.\"\"\"\n",
        "    if self.action is not None: \n",
        "      return self.action\n",
        "    return np.random.randint(self._num_actions)\n",
        "\n",
        "  def observe_first(self, timestep):\n",
        "    \"\"\"Does not record as the RandomAgent has no use for data.\"\"\"\n",
        "    pass\n",
        "\n",
        "  def observe(self, action, next_timestep):\n",
        "    \"\"\"Does not record as the RandomAgent has no use for data.\"\"\"\n",
        "    pass\n",
        "\n",
        "  def update(self):\n",
        "    \"\"\"Does not update as the RandomAgent does not learn from data.\"\"\"\n",
        "    pass"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_X_RIcItvaVU"
      },
      "source": [
        "## Define Learnable Agents"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "T6A2pa9Zvcpz"
      },
      "outputs": [],
      "source": [
        "def dqn_make_network(action_spec: specs.DiscreteArray) -> snt.Module:\n",
        "  return snt.Sequential([\n",
        "      snt.Flatten(),\n",
        "      snt.nets.MLP([128, 128, action_spec.num_values]),\n",
        "  ])\n",
        "\n",
        "def r2d2_make_network(action_spec: specs.DiscreteArray) -> snt.Module:\n",
        "  return snt.DeepRNN([\n",
        "            snt.Flatten(),\n",
        "            snt.LSTM(hidden_size = 10),\n",
        "            snt.nets.MLP([128, 128, action_spec.num_values])\n",
        "            ])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "135ZABGG6mAw"
      },
      "source": [
        "### Get reward distribution and mean reward of the agent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "VPawgcGr6mAx"
      },
      "outputs": [],
      "source": [
        "def get_reward_distribution(agent, env, n_episodes = 1000,  episode_length = 32, log_loss = False):\n",
        "  # main loop\n",
        "  n_total_steps = 0\n",
        "  n_steps = n_episodes * episode_length\n",
        "  all_returns = []\n",
        "  for episode in range(n_episodes):\n",
        "    episode_steps = 0\n",
        "    episode_return = 0\n",
        "    episode_loss = 0\n",
        "\n",
        "    start_time = time.time()\n",
        "\n",
        "    timestep = env.reset()\n",
        "\n",
        "    # Make the first observation.\n",
        "    agent.observe_first(timestep)\n",
        "\n",
        "    # Run an episode\n",
        "    while not timestep.last():\n",
        "\n",
        "      # Generate an action from the agent's policy and step the environment.\n",
        "      action = agent.select_action(timestep.observation)\n",
        "      timestep = env.step(action)\n",
        "\n",
        "      # Have the agent observe the timestep and let the agent update itself.\n",
        "      agent.observe(action, next_timestep=timestep)\n",
        "      agent.update()\n",
        "\n",
        "      # Book-keeping.\n",
        "      episode_steps += 1\n",
        "      n_total_steps += 1\n",
        "      episode_return += timestep.reward\n",
        "\n",
        "      if log_loss:\n",
        "        episode_loss += agent.last_loss\n",
        "\n",
        "      if n_steps is not None and n_total_steps >= n_steps:\n",
        "        break\n",
        "\n",
        "    # Collect the results and combine with counts.\n",
        "    steps_per_second = episode_steps / (time.time() - start_time)\n",
        "    result = {\n",
        "        'episode': episode,\n",
        "        'episode_length': episode_steps,\n",
        "        'episode_return': episode_return,\n",
        "    }\n",
        "    if log_loss:\n",
        "      result['loss_avg'] = episode_loss/episode_steps\n",
        "\n",
        "    all_returns.append(episode_return)\n",
        "\n",
        "    display(env.plot_state())\n",
        "    # Log the given results.\n",
        "    # print(result)\n",
        "\n",
        "    if n_steps is not None and n_total_steps >= n_steps:\n",
        "      break\n",
        "\n",
        "  return all_returns, np.mean(all_returns), np.std(all_returns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DMxo_yt5uN5d"
      },
      "source": [
        "## Training and Evaluation Script"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "_cxysHVguQOG"
      },
      "outputs": [],
      "source": [
        "def train_and_eval(env, agent, train = False, n_episodes = 1000,  episode_length = 32, log_loss = False):\n",
        "  if train:\n",
        "    loop = EnvironmentLoop(env, agent, logger=loggers.InMemoryLogger())\n",
        "    loop.run(n_episodes * 100)\n",
        "\n",
        "  all_returns, mean_return, std_return = get_reward_distribution(agent, env, n_episodes = 1000,  episode_length = 32, log_loss = False)\n",
        "  clear_output()\n",
        "  print(f'Mean Return: {mean_return} | Standard Deviation: {std_return}')\n",
        "  plt.figure()\n",
        "  sns.histplot(all_returns, stat=\"density\", kde=True, bins=12)\n",
        "  plt.xlabel('Return [a.u.]')\n",
        "  plt.ylabel('Density')\n",
        "  plt.show()\n",
        "\n",
        "  if train:\n",
        "    return pd.DataFrame(loop._logger._data)\n",
        "  else:\n",
        "    return None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_EGCq1_c6mAu"
      },
      "source": [
        "### Initialize the environments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuD4BchT6mAv",
        "outputId": "6f294767-1810-42d9-8b49-8bd367ec1e50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "actions:\n",
            " DiscreteArray(shape=(), dtype=int32, name=action, minimum=0, maximum=1, num_values=2)\n",
            "observations:\n",
            " BoundedArray(shape=(32,), dtype=dtype('float32'), name='nback_stimuli', minimum=0.0, maximum=7.0)\n",
            "rewards:\n",
            " Array(shape=(), dtype=dtype('float32'), name='reward')\n"
          ]
        }
      ],
      "source": [
        "env_balanced, env_spec_balanced = NBack.create_environment()\n",
        "env_unbalanced, env_spec_unbalanced = NBack.create_environment(prob = 0.)\n",
        "\n",
        "print('actions:\\n', env_spec_balanced.actions)\n",
        "print('observations:\\n', env_spec_balanced.observations)\n",
        "print('rewards:\\n', env_spec_balanced.rewards)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukAvPj94hwOJ"
      },
      "source": [
        "# Define Agents"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "XcvCnGfDhxIC"
      },
      "outputs": [],
      "source": [
        "random_agent = RandomAgent(env_spec_balanced)\n",
        "match_agent = RandomAgent(env_spec_balanced, 0)\n",
        "unmatch_agent = RandomAgent(env_spec_balanced, 1)\n",
        "\n",
        "dqn_agent_balanced = dqn.DQN(\n",
        "    environment_spec=env_spec_balanced,\n",
        "    network=dqn_make_network(env_spec_balanced.actions),\n",
        "    epsilon=[0.5],\n",
        "    learning_rate = 0.0001,\n",
        "    min_replay_size = 32,\n",
        "    logger=loggers.InMemoryLogger(),\n",
        "    checkpoint=False,\n",
        ")\n",
        "\n",
        "dqn_agent_unbalanced = dqn.DQN(\n",
        "    environment_spec=env_spec_unbalanced,\n",
        "    network=dqn_make_network(env_spec_unbalanced.actions),\n",
        "    epsilon=[0.5],\n",
        "    learning_rate = 0.0001,\n",
        "    min_replay_size = 32,\n",
        "    logger=loggers.InMemoryLogger(),\n",
        "    checkpoint=False,\n",
        ")\n",
        "\n",
        "drqn_agent_balanced = r2d2.R2D2(\n",
        "    environment_spec=env_spec_balanced,\n",
        "    network=r2d2_make_network(env_spec_balanced.actions),\n",
        "    learning_rate = 0.0001,\n",
        "    burn_in_length=10,\n",
        "    trace_length=2,\n",
        "    replay_period=10,\n",
        "    epsilon=[0.05],\n",
        "    logger=loggers.InMemoryLogger(),\n",
        "    min_replay_size = 32,\n",
        "    checkpoint=False,\n",
        ")\n",
        "\n",
        "drqn_agent_unbalanced = r2d2.R2D2(\n",
        "    environment_spec=env_spec_unbalanced,\n",
        "    network=r2d2_make_network(env_spec_unbalanced.actions),\n",
        "    learning_rate = 0.0001,\n",
        "    burn_in_length=10,\n",
        "    trace_length=2,\n",
        "    replay_period=10,\n",
        "    epsilon=[0.05],\n",
        "    logger=loggers.InMemoryLogger(),\n",
        "    min_replay_size = 32,\n",
        "    checkpoint=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "x4yOQ6aY6P9K"
      },
      "outputs": [],
      "source": [
        "n_episodes = 250\n",
        "episode_length = 32"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tI_K_1zDyaz7"
      },
      "source": [
        "## Random Agent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SURbpHPeydLy"
      },
      "source": [
        "### Balanced N-Back"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "7KqHQnnXw-oX",
        "outputId": "0a773bff-ec28-4c16-d82c-01773ec0638b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Return: -15.595 | Standard Deviation: 2.74644770567364\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_and_eval(env_balanced, random_agent, n_episodes = n_episodes,  episode_length = episode_length, log_loss = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPQgEENVyf8O"
      },
      "source": [
        "### Un-Balanced N-Back"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "4eDnhTWYyBbM",
        "outputId": "2c0d43bc-bcf7-4ecb-d2d7-b3870ff06bb1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Return: -15.569 | Standard Deviation: 2.7887701590486085\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_and_eval(env_unbalanced, random_agent, n_episodes = n_episodes,  episode_length = episode_length, log_loss = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KHlNxPbMyhzS"
      },
      "source": [
        "## Matching Agent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhblKbMfyjmh"
      },
      "source": [
        "### Balanced N-Back"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "iSwcN1cHyEJf",
        "outputId": "57934e91-5379-480c-a473-aa9c5386c5bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Return: -16.247 | Standard Deviation: 2.7960670592816617\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_and_eval(env_balanced, match_agent, n_episodes = n_episodes,  episode_length = episode_length, log_loss = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JO5ZCYnHylS0"
      },
      "source": [
        "### Un-Balanced N-Back"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "AfdlslUeyEg3",
        "outputId": "f53585c9-6375-4cfe-9884-6d0e7ea04827"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Return: -25.895 | Standard Deviation: 2.046942842387154\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_and_eval(env_unbalanced, match_agent, n_episodes = n_episodes,  episode_length = episode_length, log_loss = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9KqkvKRyoO4"
      },
      "source": [
        "## Un-Matching Agent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-jRSzfu2yqQP"
      },
      "source": [
        "### Balanced N-Back"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "CF2AnpQiyJxl",
        "outputId": "cb3aaf63-1fe9-4bc9-ad06-3a4effbd7f92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Return: -14.813 | Standard Deviation: 2.7459116883104597\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_and_eval(env_balanced, unmatch_agent, n_episodes = n_episodes,  episode_length = episode_length, log_loss = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lziRjJDYyrD2"
      },
      "source": [
        "### Un-Balanced N-Back"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "eULQxh4KyKGB",
        "outputId": "2a84a88b-5909-4bac-b21d-7c3656b3ad0e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Return: -5.199 | Standard Deviation: 2.0142986372432463\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_and_eval(env_unbalanced, unmatch_agent, n_episodes = n_episodes,  episode_length = episode_length, log_loss = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ji6I5b0zys59"
      },
      "source": [
        "## DQN Agent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1tCSFK6RyuPb"
      },
      "source": [
        "### Balanced N-Back"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "TR4-WWlQyOAc",
        "outputId": "2c0f2af7-2524-4a5a-d97b-7a6e60b00257"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Return: -13.678 | Standard Deviation: 2.7331878823088616\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "logs_dqn_balanced = train_and_eval(env_balanced, dqn_agent_balanced, True, n_episodes = n_episodes,  episode_length = episode_length, log_loss = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRApxz2xyvIn"
      },
      "source": [
        "### Un-Balanced N-Back"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "RbHSBs9JyOPL",
        "outputId": "0743b51d-d1d0-43ab-a462-94aa1501314e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Return: -10.3 | Standard Deviation: 2.6237377917772196\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "logs_dqn_unbalanced = train_and_eval(env_unbalanced, dqn_agent_unbalanced, True, n_episodes = n_episodes,  episode_length = episode_length, log_loss = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VYENVrXxywgf"
      },
      "source": [
        "## DRQN Agent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PI2ZXu7Kyxyc"
      },
      "source": [
        "### Balanced N-Back"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "FKEeEbjByWrp",
        "outputId": "1243fdca-1d35-46b8-b4b7-61772c000ee9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Return: -14.32 | Standard Deviation: 2.543540839066674\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "logs_drqn_balanced = train_and_eval(env_balanced, drqn_agent_balanced, True, n_episodes = n_episodes,  episode_length = episode_length, log_loss = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7hLLNggyyRG"
      },
      "source": [
        "### Un-Balanced N-Back"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "y0Kz9jPtyW78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "818434ac-0598-4788-93ff-8e2ed22bbdf8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Return: -5.963 | Standard Deviation: 2.2946962761986605\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "logs_drqn_unbalanced = train_and_eval(env_unbalanced, drqn_agent_unbalanced, True, n_episodes = n_episodes,  episode_length = episode_length, log_loss = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "uro52jc69oCH"
      },
      "outputs": [],
      "source": [
        "def moving_average(interval, window_size):\n",
        "    window = np.ones(int(window_size)) / float(window_size)\n",
        "    return np.convolve(interval, window, 'same')  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "gLIG5S-K1tB-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "1c6880d1-41d5-4066-acd8-805a170f4ed8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Equal match and non-match')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Balanced Logs\n",
        "dqn_b_ma = moving_average(logs_dqn_balanced['episode_return'], 100)\n",
        "drqn_b_ma = moving_average(logs_drqn_balanced['episode_return'], 100)\n",
        "\n",
        "# dqn_b = list(logs_dqn_balanced['episode_return'])\n",
        "# drqn_b = list(logs_drqn_balanced['episode_return'])\n",
        "\n",
        "plt.plot(dqn_b_ma, color='blue')\n",
        "plt.plot(drqn_b_ma, color='yellow')\n",
        "\n",
        "# plt.plot(dqn_b, color='blue')\n",
        "# plt.plot(drqn_b, color='yellow')\n",
        "\n",
        "plt.ylabel(\"episode_reward\")\n",
        "plt.xlabel(\"episode (N_steps per episode = 32)\")\n",
        "plt.legend(['DQN','DRQN'])\n",
        "plt.title(\"Equal match and non-match\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "v1CkmCXg1uLI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "a9678642-9149-40c8-e7be-77fa1a850afa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Unequal match and non-match')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Un-Balanced Logs\n",
        "dqn_u_ma = moving_average(logs_dqn_unbalanced['episode_return'], 100)\n",
        "drqn_u_ma = moving_average(logs_drqn_unbalanced['episode_return'], 100)\n",
        "\n",
        "# dqn_u = list(logs_dqn_unbalanced['episode_return'])\n",
        "# drqn_u = list(logs_drqn_unbalanced['episode_return'])\n",
        "\n",
        "plt.plot(dqn_u_ma, color='blue')\n",
        "plt.plot(drqn_u_ma, color='yellow')\n",
        "\n",
        "# plt.plot(dqn_u, color='blue')\n",
        "# plt.plot(drqn_u, color='yellow')\n",
        "\n",
        "plt.ylabel(\"episode_reward\")\n",
        "plt.xlabel(\"episode (N_steps per episode = 32)\")\n",
        "plt.legend(['DQN','DRQN'])\n",
        "plt.title(\"Unequal match and non-match\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Modeling Cognitive Tasks",
      "provenance": [],
      "toc_visible": true
    },
    "gpuClass": "standard",
    "kernel": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}