DMARL with TF-Agents.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Agent Reinforcement Learning with TF-Agents\n",
    "\n",
    "In this notebook we're going to be implementing reinforcement learning (RL) agents to play games against one another. Before reading this it is advised to be familiar with the [TF-Agents](https://github.com/tensorflow/agents) and Deep Q-Learning; [this tutorial](https://github.com/tensorflow/agents/blob/master/docs/tutorials/1_dqn_tutorial.ipynb) will bring you up to speed.\n",
    "\n",
    "### Introduction\n",
    "\n",
    "TF-Agents is a framework for designing and experimenting with RL algorithms. It provides a collection of useful abstractions such as agents, replay buffers, and drivers. However, the code is quite rigidly tied to the single-agent view, which is explained by the *extrinsically motivated* agent in the diagram below.\n",
    "\n",
    "In this view, the environment provides observations and rewards to the agent. Under the assumption that there is only one agent this makes sense, however, when we have many agents in the same space we would like to have agent-specific observations and rewards. In order to rectify this we first need to think of agents as *intrinsically motivated*, which is to say that their rewards are a function of their observations and internal state. Secondly, the agent is only *partially observing* the environment, and the window into the environment is a function of the agent's total state. This total state can include \"physical\" properties of the agent such as position, but it also includes internal state. For example, an agent could have an internal `is_sleeping` parameter that multiplies their observations by zero to simulate a lack of light.\n",
    "\n",
    "### Intrinsic Motivation\n",
    "\n",
    "In order to implement this with TF-Agents we are going to define an `IMAgent` (Intrinsically Motivated Agent) class by overriding the `DqnAgent` class. In the standard TF-Agents DQN pipeline the agent is trained by alternating between data collection and training updates to the Q-Network. Data collection is done with a special `collect_policy` which behaves differently to the main policy for the sake of managing the exploitation-exploration trade-off. Usually, the environment and the agent are separated. The environment generates a `TimeStep` containing the observation and reward information which is then passed to `policy.action`. This produces a `PolicyStep` that contains an action to step the environment. \n",
    "\n",
    "<p align=\"center\">\n",
    "<img src=\"./im_rl_agent.png\" width=\"600px\" display=\"block\" margin-left=\"auto\" margin-right=\"auto\" class=\"center\"/>\n",
    "</p>\n",
    "\n",
    "This provides us with two approaches to our problem. We could make the enviroment aware of which agent it is producing the `TimeStep` for, or we could have each agent ingest an agent-independent time step that is then augmented internally. Here we argue that the latter is a more natural decomposition as it keeps the agent-specific code with the agent class. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running this Code\n",
    "\n",
    "The following is the list of imports that we will need for this tutorial. In order to run the code yourself you should clone [the repository](https://github.com/DylanCope/Multi-Agent-RL-with-TF) from GitHub and use [Anaconda](https://docs.conda.io/en/latest/) and the `env.yaml` file to [recreate the Python environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Physical Devices:\n",
      " [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')] \n",
      "\n",
      "\n",
      "Output Directory: ./outputs/15923139163402452\n"
     ]
    }
   ],
   "source": [
    "from functools import partial\n",
    "from IPython.display import clear_output\n",
    "from itertools import cycle\n",
    "from pathlib import Path\n",
    "import random\n",
    "from time import time\n",
    "from typing import Tuple, List, Callable\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "import tensorflow as tf\n",
    "from tf_agents.agents import DqnAgent\n",
    "from tf_agents.agents.tf_agent import LossInfo\n",
    "from tf_agents.environments.py_environment import PyEnvironment\n",
    "from tf_agents.environments.tf_py_environment import TFPyEnvironment\n",
    "from tf_agents.networks.q_rnn_network import QRnnNetwork\n",
    "from tf_agents.networks.q_network import QNetwork\n",
    "from tf_agents.replay_buffers.tf_uniform_replay_buffer import TFUniformReplayBuffer\n",
    "from tf_agents.specs import TensorSpec\n",
    "from tf_agents.trajectories import trajectory\n",
    "from tf_agents.trajectories.time_step import TimeStep\n",
    "from tf_agents.trajectories.trajectory import Trajectory\n",
    "from tf_agents.utils import common\n",
    "\n",
    "print('Physical Devices:\\n', tf.config.list_physical_devices(), '\\n\\n')\n",
    "\n",
    "OUTPUTS_DIR = f'./outputs/{int(10000000 * time())}'\n",
    "print('Output Directory:', OUTPUTS_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the IMAgent Class\n",
    "\n",
    "In the next cell we will be defining the `IMAgent` class. This class is designed to be relatively general, but it could be improved by adding support for other reinforcement learning algorithms. The most natural way to do this would be by composition, rather than inheritance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class IMAgent(DqnAgent):\n",
    "\n",
    "    def __init__(self,\n",
    "                 env: TFPyEnvironment,\n",
    "                 observation_spec: TensorSpec = None,\n",
    "                 action_spec: TensorSpec = None,\n",
    "                 reward_fn: Callable = lambda time_step: time_step.reward,\n",
    "                 action_fn: Callable = lambda action: action,\n",
    "                 name: str='IMAgent',\n",
    "                 q_network=None,\n",
    "                 # training params\n",
    "                 replay_buffer_max_length: int = 1000,\n",
    "                 learning_rate: float = 1e-5,\n",
    "                 training_batch_size: int = 8,\n",
    "                 training_parallel_calls: int = 3,\n",
    "                 training_prefetch_buffer_size: int = 3,\n",
    "                 training_num_steps: int = 2,\n",
    "                 **dqn_kwargs):\n",
    "\n",
    "        self._env = env\n",
    "        self._reward_fn = reward_fn\n",
    "        self._name = name\n",
    "        self._observation_spec = observation_spec or self._env.observation_spec()\n",
    "        self._action_spec = action_spec or self._env.action_spec()\n",
    "        self._action_fn = action_fn\n",
    "\n",
    "        q_network = q_network or self._build_q_net()\n",
    "\n",
    "        env_ts_spec = self._env.time_step_spec()\n",
    "        time_step_spec = TimeStep(\n",
    "            step_type=env_ts_spec.step_type,\n",
    "            reward=env_ts_spec.reward,\n",
    "            discount=env_ts_spec.discount,\n",
    "            observation=q_network.input_tensor_spec\n",
    "        )\n",
    "\n",
    "        optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n",
    "        super().__init__(time_step_spec,\n",
    "                         self._action_spec,\n",
    "                         q_network,\n",
    "                         optimizer,\n",
    "                         name=name,\n",
    "                         **dqn_kwargs)\n",
    "\n",
    "        self._policy_state = self.policy.get_initial_state(\n",
    "            batch_size=self._env.batch_size)\n",
    "        self._rewards = []\n",
    "\n",
    "        self._replay_buffer = TFUniformReplayBuffer(\n",
    "            data_spec=self.collect_data_spec,\n",
    "            batch_size=self._env.batch_size,\n",
    "            max_length=replay_buffer_max_length)\n",
    "        \n",
    "        self._training_batch_size = training_batch_size\n",
    "        self._training_parallel_calls = training_parallel_calls\n",
    "        self._training_prefetch_buffer_size = training_prefetch_buffer_size\n",
    "        self._training_num_steps = training_num_steps\n",
    "        self.train = common.function(self.train)\n",
    "\n",
    "    def _build_q_net(self):\n",
    "#         q_net = QRnnNetwork(input_tensor_spec=self._observation_spec,\n",
    "#                             action_spec=self._action_spec,\n",
    "#                             name=f'{self._name}QRNN')\n",
    "\n",
    "        fc_layer_params = (50,)\n",
    "\n",
    "        q_net = QNetwork(\n",
    "            self._observation_spec,\n",
    "            self._action_spec,\n",
    "            fc_layer_params=fc_layer_params)\n",
    "\n",
    "        q_net.create_variables()\n",
    "        q_net.summary()\n",
    "        \n",
    "        return q_net\n",
    "\n",
    "    def reset(self):\n",
    "        self._policy_state = self.policy.get_initial_state(\n",
    "            batch_size=self._env.batch_size\n",
    "        )\n",
    "        self._rewards = []\n",
    "        \n",
    "    def episode_return(self) -> float:\n",
    "        return np.sum(self._rewards)\n",
    "        \n",
    "    def _observation_fn(self, observation: tf.Tensor) -> tf.Tensor:\n",
    "        \"\"\"\n",
    "            Takes a tensor with specification self._env.observation_spec\n",
    "            and extracts a tensor with specification self._observation_spec.\n",
    "            \n",
    "            For example, consider an agent within an NxN maze environment. \n",
    "            The env could expose the entire NxN integer matrix as an observation\n",
    "            but we would prefer the agent to only see a 3x3 window around their\n",
    "            current location. To do this we can override this method.\n",
    "            \n",
    "            This allows us to have different agents acting in the same environment\n",
    "            with different observations.\n",
    "        \"\"\"\n",
    "        return observation\n",
    "\n",
    "    def _augment_time_step(self, time_step: TimeStep) -> TimeStep:\n",
    "\n",
    "        reward = self._reward_fn(time_step)\n",
    "        reward = tf.convert_to_tensor(reward, dtype=tf.float32)\n",
    "        if reward.shape != time_step.reward.shape:\n",
    "            reward = tf.reshape(reward, time_step.reward.shape)\n",
    "            \n",
    "        observation = self._observation_fn(time_step.observation)\n",
    "\n",
    "        return TimeStep(\n",
    "            step_type=time_step.step_type,\n",
    "            reward=reward,\n",
    "            discount=time_step.discount,\n",
    "            observation=observation\n",
    "        )\n",
    "\n",
    "    def _current_time_step(self) -> TimeStep:\n",
    "        time_step = self._env.current_time_step()\n",
    "        time_step = self._augment_time_step(time_step)\n",
    "        return time_step\n",
    "\n",
    "    def _step_environment(self, action) -> TimeStep:\n",
    "        action = self._action_fn(action)\n",
    "        time_step = self._env.step(action)\n",
    "        time_step = self._augment_time_step(time_step)\n",
    "        return time_step\n",
    "\n",
    "    def act(self, collect=False) -> Trajectory:\n",
    "        time_step = self._current_time_step()\n",
    "\n",
    "        if collect:\n",
    "            policy_step = self.collect_policy.action(\n",
    "                time_step, policy_state=self._policy_state)\n",
    "        else:\n",
    "            policy_step = self.policy.action(\n",
    "                time_step, policy_state=self._policy_state)\n",
    "\n",
    "        self._policy_state = policy_step.state\n",
    "        next_time_step = self._step_environment(policy_step.action)\n",
    "        traj = trajectory.from_transition(time_step, policy_step, next_time_step)\n",
    "\n",
    "        self._rewards.append(next_time_step.reward)\n",
    "        \n",
    "        if collect:\n",
    "            self._replay_buffer.add_batch(traj)\n",
    "\n",
    "        return traj\n",
    "\n",
    "    def train_iteration(self) -> LossInfo:\n",
    "        experience, info = self._replay_buffer.get_next(\n",
    "            sample_batch_size=self._training_batch_size,\n",
    "            num_steps=self._training_num_steps\n",
    "        )\n",
    "        return self.train(experience)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tic-Tac-Toe \n",
    "\n",
    "<p align=\"center\">\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/32/Tic_tac_toe.svg/1200px-Tic_tac_toe.svg.png\" width=\"300px\" class=\"center\"/>\n",
    "</p>\n",
    "\n",
    "In order to test this we can utlise the [already-implemented Tic-Tac-Toe environment](https://github.com/tensorflow/agents/blob/master/tf_agents/environments/examples/tic_tac_toe_environment.py) in TF-Agents (At the time of writing this script has not been added to the pip distribution so I have manually copied it across). The environment represents the problem on a 3x3 matrix where a 0 represents an empty slot, a 1 represents a play by player 1, and a 2 represents a play by player 2. However, as TF-Agents is not focused on the multi-agent case, their implementation has the second player act randomly. To change this we will override the step function.\n",
    "\n",
    "The only additional change that we need to make is to the action specification, where we need to provide the value that is being placed (i.e. which player is making the move)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tic_tac_toe_environment import TicTacToeEnvironment\n",
    "from tf_agents.specs import BoundedArraySpec\n",
    "from tf_agents.trajectories.time_step import StepType\n",
    "\n",
    "REWARD_ILLEGAL_MOVE = np.asarray(-5, dtype=np.float32)\n",
    "\n",
    "class TicTacToeMultiAgentEnv(TicTacToeEnvironment):\n",
    "    \n",
    "    def action_spec(self):\n",
    "        position_spec = BoundedArraySpec((), np.int32, minimum=0, maximum=8)\n",
    "        value_spec = BoundedArraySpec((), np.int32, minimum=1, maximum=2)\n",
    "        return {\n",
    "            'position': position_spec,\n",
    "            'value': value_spec\n",
    "        }\n",
    "    \n",
    "    def _step(self, action: np.ndarray):\n",
    "        if self._current_time_step.is_last():\n",
    "            return self._reset()\n",
    "\n",
    "        index_flat = np.array(range(9)) == action['position']\n",
    "        index = index_flat.reshape(self._states.shape) == True\n",
    "        if self._states[index] != 0:\n",
    "            return TimeStep(StepType.LAST, \n",
    "                            REWARD_ILLEGAL_MOVE,\n",
    "                            self._discount, \n",
    "                            self._states)\n",
    "\n",
    "        self._states[index] = action['value']\n",
    "\n",
    "        is_final, reward = self._check_states(self._states)\n",
    "        \n",
    "        if np.all(self._states == 0):\n",
    "            step_type = StepType.FIRST\n",
    "        elif is_final:\n",
    "            step_type = StepType.LAST\n",
    "        else:\n",
    "            step_type = StepType.MID\n",
    "\n",
    "        return TimeStep(step_type, reward, self._discount, self._states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_tic_tac_toe(state):\n",
    "    table_str = '''\n",
    "    {} | {} | {}\n",
    "    - + - + -\n",
    "    {} | {} | {}\n",
    "    - + - + -\n",
    "    {} | {} | {}\n",
    "    '''.format(*tuple(state.flatten()))\n",
    "    table_str = table_str.replace('0', ' ')\n",
    "    table_str = table_str.replace('1', 'X')\n",
    "    table_str = table_str.replace('2', 'O')\n",
    "    print(table_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a test of the environment we will step the environment with random actions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reward: 0.0 Board:\n",
      "\n",
      "      |   |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: 1 Action: 2 Reward: 0.0 Board:\n",
      "\n",
      "      |   | X\n",
      "    - + - + -\n",
      "      |   |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: 2 Action: 1 Reward: 0.0 Board:\n",
      "\n",
      "      | O | X\n",
      "    - + - + -\n",
      "      |   |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: 1 Action: 4 Reward: 0.0 Board:\n",
      "\n",
      "      | O | X\n",
      "    - + - + -\n",
      "      | X |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: 2 Action: 1 Reward: -5.0 Board:\n",
      "\n",
      "      | O | X\n",
      "    - + - + -\n",
      "      | X |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n"
     ]
    }
   ],
   "source": [
    "tic_tac_toe_env = TicTacToeMultiAgentEnv()\n",
    "\n",
    "ts = tic_tac_toe_env.reset()\n",
    "print('Reward:', ts.reward, 'Board:')\n",
    "print_tic_tac_toe(ts.observation)\n",
    "\n",
    "random.seed(1)\n",
    "player = 1\n",
    "while not ts.is_last():\n",
    "    action = {\n",
    "        'position': np.asarray(random.randint(0, 8)),\n",
    "        'value': player\n",
    "    }\n",
    "    ts = tic_tac_toe_env.step(action)\n",
    "    print('Player:', player, 'Action:', action['position'],\n",
    "          'Reward:', ts.reward, 'Board:')\n",
    "    print_tic_tac_toe(ts.observation)\n",
    "    player = 1 + player % 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Player Objects\n",
    "\n",
    "Each of our players will be instances of `IMAgent` with a differing action and reward functions. The actions that the players make are dictionaries with position and value items, specifying what marking the player is trying to make on the board. Player 1 will get the default +1 for a win and -1 for a loss, but will will need to invert these rewards for Player 2.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"QNetwork\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "EncodingNetwork (EncodingNet multiple                  500       \n",
      "_________________________________________________________________\n",
      "dense (Dense)                multiple                  459       \n",
      "=================================================================\n",
      "Total params: 959\n",
      "Trainable params: 959\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"QNetwork\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "EncodingNetwork (EncodingNet multiple                  500       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              multiple                  459       \n",
      "=================================================================\n",
      "Total params: 959\n",
      "Trainable params: 959\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def ttt_action_fn(player, action):\n",
    "    return {'position': action, 'value': player}\n",
    "\n",
    "tf_ttt_env = TFPyEnvironment(tic_tac_toe_env)\n",
    "\n",
    "player_1 = IMAgent(\n",
    "    tf_ttt_env,\n",
    "    action_spec = tf_ttt_env.action_spec()['position'],\n",
    "    action_fn = partial(ttt_action_fn, 1),\n",
    "    name='Player1'\n",
    ")\n",
    "\n",
    "def p2_reward_fn(ts: TimeStep) -> float:\n",
    "    if ts.reward == -1.0:\n",
    "        return 1.0\n",
    "    if ts.reward == 1.0:\n",
    "        return -1.0\n",
    "    return ts.reward\n",
    "\n",
    "player_2 = IMAgent(\n",
    "    tf_ttt_env,\n",
    "    action_spec = tf_ttt_env.action_spec()['position'],\n",
    "    action_fn = partial(ttt_action_fn, 2),\n",
    "    reward_fn = p2_reward_fn,\n",
    "    name='Player2'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random start board:\n",
      "\n",
      "      | X |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: Player2, Reward: 0.0\n",
      "\n",
      "      | X |  \n",
      "    - + - + -\n",
      "      |   | O\n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: Player1, Reward: 0.0\n",
      "\n",
      "      | X | X\n",
      "    - + - + -\n",
      "      |   | O\n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: Player2, Reward: -5.0\n",
      "\n",
      "      | X | X\n",
      "    - + - + -\n",
      "      |   | O\n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n"
     ]
    }
   ],
   "source": [
    "ts = tf_ttt_env.reset()\n",
    "\n",
    "# arbitrary starting point to add variety\n",
    "random.seed(1)\n",
    "start_player_id = random.randint(1, 2)\n",
    "tf_ttt_env.step({\n",
    "    'position': tf.convert_to_tensor([random.randint(0, 8)]), \n",
    "    'value': start_player_id\n",
    "})\n",
    "ts = tf_ttt_env.current_time_step()\n",
    "print('Random start board:')\n",
    "print_tic_tac_toe(ts.observation.numpy())\n",
    "\n",
    "if start_player_id == 2:\n",
    "    players = cycle([player_1, player_2])\n",
    "else:\n",
    "    players = cycle([player_2, player_1])\n",
    "    \n",
    "while not ts.is_last():\n",
    "    player = next(players)\n",
    "    player.act()\n",
    "    ts = tf_ttt_env.current_time_step()\n",
    "    print(f'Player: {player.name}, Reward: {ts.reward[0]}')\n",
    "    print_tic_tac_toe(ts.observation.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the agents properly interact with the environment, even if they are currently unable to perform well.\n",
    "\n",
    "In order to verify that the end condition rewards are all correct, we will engineer a winning state for each player and print out the appropriate information: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    O |   |  \n",
      "    - + - + -\n",
      "      | O |  \n",
      "    - + - + -\n",
      "      |   | O\n",
      "    \n",
      "Is Final Step: True\n",
      "Player 1 Reward: -1.0\n",
      "Player 2 Reward: 1.0\n"
     ]
    }
   ],
   "source": [
    "ts = tf_ttt_env.reset()\n",
    "\n",
    "for pos in [0, 4, 8]:\n",
    "    tf_ttt_env.step({\n",
    "        'position': tf.convert_to_tensor([pos]), \n",
    "        'value': 2\n",
    "    })\n",
    "\n",
    "ts = tf_ttt_env.current_time_step()\n",
    "print_tic_tac_toe(ts.observation.numpy())\n",
    "print('Is Final Step:', TimeStep.is_last(ts).numpy()[0])\n",
    "print('Player 1 Reward:', player_1._augment_time_step(ts).reward.numpy()[0])\n",
    "print('Player 2 Reward:', player_2._augment_time_step(ts).reward.numpy()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    X |   |  \n",
      "    - + - + -\n",
      "      | X |  \n",
      "    - + - + -\n",
      "      |   | X\n",
      "    \n",
      "Is Final Step: True\n",
      "Player 1 Reward: 1.0\n",
      "Player 2 Reward: -1.0\n"
     ]
    }
   ],
   "source": [
    "ts = tf_ttt_env.reset()\n",
    "\n",
    "for pos in [0, 4, 8]:\n",
    "    tf_ttt_env.step({\n",
    "        'position': tf.convert_to_tensor([pos]), \n",
    "        'value': 1\n",
    "    })\n",
    "\n",
    "ts = tf_ttt_env.current_time_step()\n",
    "print_tic_tac_toe(ts.observation.numpy())\n",
    "print('Is Final Step:', TimeStep.is_last(ts).numpy()[0])\n",
    "print('Player 1 Reward:', player_1._augment_time_step(ts).reward.numpy()[0])\n",
    "print('Player 2 Reward:', player_2._augment_time_step(ts).reward.numpy()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Agents\n",
    "\n",
    "<p align=\"center\">\n",
    "<img src=\"https://miro.medium.com/max/2784/1*_5dltx4BcI8rRmCK2Sq_kw.png\" width=\"500px\" class=\"center\"/>\n",
    "</p>\n",
    "\n",
    "In order to train the agents we will do a iterations of 'episodes' and training steps. Each episode is a game of Tic Tac Toe where the agents use their 'collect policies' and store the trajectories that they experience in replay buffers. These collect policies are *epsilon greedy*: some percentage of the time (10\\% in our case) the agents will perform a random action rather then following the best suggestion of the Q-networks. This is to balance the [exploration-exploitation trade-off](https://towardsdatascience.com/intuition-exploration-vs-exploitation-c645a1d37c7a). The agents are *Independent Q-learners* (IQL), meaning that they do not share parameters or experience. This means that each store their experience to their own buffers and separately use these datasets to update thier own weights. A side-effect of IQL is that the agents may learn strategies that the other has not, leading to chaotic learning dynamics where the loss scores of each agent fluctuate in tandem with one another throughout the training iterations.  \n",
    "\n",
    "The following cell defines the logic of a single training episode where we randomly pick one of the two players to go first. This is important because Tic Tac Toe is a [*solved game*](https://en.wikipedia.org/wiki/Solved_game): whoever plays first can force a win or a draw if they play optimally. Therefore, as the agents master the game we should expect the win rate to trend towards 50\\% (when the $\\epsilon$-greedy policy is not in use)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_episode(tf_ttt_env, player_1, player_2):\n",
    "    ts = tf_ttt_env.reset()\n",
    "    player_1.reset()\n",
    "    player_2.reset()\n",
    "    time_steps = []\n",
    "    if bool(random.randint(0, 1)):\n",
    "        players = cycle([player_1, player_2])\n",
    "    else:\n",
    "        players = cycle([player_2, player_1])\n",
    "    while not ts.is_last():\n",
    "        player = next(players)\n",
    "        player.act(collect=True)\n",
    "        ts = tf_ttt_env.current_time_step()\n",
    "        time_steps.append(ts)\n",
    "    return time_steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting the Trajectories\n",
    "\n",
    "First, lets verify that the trajectories are being written to the buffers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of trajectories recorded by P1: 27\n",
      "Number of trajectories recorded by P2: 27\n"
     ]
    }
   ],
   "source": [
    "for _ in range(10):\n",
    "    training_episode(tf_ttt_env, player_1, player_2)\n",
    "\n",
    "print('Number of trajectories recorded by P1:',\n",
    "      player_1._replay_buffer.num_frames().numpy())\n",
    "print('Number of trajectories recorded by P2:',\n",
    "      player_2._replay_buffer.num_frames().numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To better understand what is being stored in the buffers, lets visualising a sample from Player 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action: Place 'X' at (2, 0)\n",
      "Reward: -5.0\n",
      "Trajectory:\n",
      "      Start             End\n",
      "    O |   | X          |   |  \n",
      "    - + - + -        - + - + -\n",
      "    O |   |     ->     |   |  \n",
      "    - + - + -        - + - + -\n",
      "    X |   | O          |   |  \n",
      "    \n",
      "\n",
      "Action: Place 'X' at (0, 0)\n",
      "Reward: 0.0\n",
      "Trajectory:\n",
      "      Start             End\n",
      "      |   |          X |   |  \n",
      "    - + - + -        - + - + -\n",
      "      |   |     ->   O |   |  \n",
      "    - + - + -        - + - + -\n",
      "      |   |            |   |  \n",
      "    \n",
      "\n",
      "Action: Place 'X' at (0, 2)\n",
      "Reward: 0.0\n",
      "Trajectory:\n",
      "      Start             End\n",
      "    O |   |          O |   |  \n",
      "    - + - + -        - + - + -\n",
      "      |   |     ->   O |   |  \n",
      "    - + - + -        - + - + -\n",
      "      |   |          X |   |  \n",
      "    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "tf.random.set_seed(5)\n",
    "traj_batches, info = player_1._replay_buffer.get_next(\n",
    "    num_steps=2, sample_batch_size=3)\n",
    "\n",
    "def print_traj(traj):\n",
    "    steps = tf.concat(list(traj), axis=-1)\n",
    "    table_str = '''Trajectory:\n",
    "      Start             End\n",
    "    {} | {} | {}        {} | {} | {}\n",
    "    - + - + -        - + - + -\n",
    "    {} | {} | {}   ->   {} | {} | {}\n",
    "    - + - + -        - + - + -\n",
    "    {} | {} | {}        {} | {} | {}\n",
    "    '''.format(*tuple(steps.numpy().flatten()))\n",
    "    table_str = table_str.replace('0', ' ')\n",
    "    table_str = table_str.replace('1', 'X')\n",
    "    table_str = table_str.replace('2', 'O')\n",
    "    print(table_str)\n",
    "    \n",
    "for i in range(3):\n",
    "    action = traj_batches.action[i, 0].numpy()\n",
    "    print('Action: Place \\'X\\' at', \n",
    "          (action % 3, action // 3))\n",
    "    reward = traj_batches.reward[i].numpy()\n",
    "    print('Reward:', reward)\n",
    "    print_traj(traj_batches.observation[i])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The replay buffer `get_next` method allows us to choose the size of the batch and the number of time steps in each sample. Here we have three trajectories, each showing one transition. We can see that from the perspective of Player 1 the other agent's actions just appear in the next time step.\n",
    "\n",
    "\n",
    "## Training Loops and Plotting\n",
    "\n",
    "In this next cell we define the core training loop and history plotting functions. For each episode we are recording data on the outcome, and player returns (sum of rewards over the episode) and other metadata. For each training step we are recording loss information. We are then taking all this information and creating plots that get created and updated at regular intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_training_data():\n",
    "    for game in range(episodes_per_iteration):\n",
    "        training_episode(tf_ttt_env, player_1, player_2)\n",
    "\n",
    "        p1_return = player_1.episode_return()\n",
    "        p2_return =  player_2.episode_return()\n",
    "\n",
    "        if REWARD_ILLEGAL_MOVE in [p1_return, p2_return]:\n",
    "            outcome = 'illegal'\n",
    "        elif p1_return == TicTacToeEnvironment.REWARD_WIN:\n",
    "            outcome = 'p1_win'\n",
    "        elif p2_return == TicTacToeEnvironment.REWARD_WIN:\n",
    "            outcome = 'p2_win'\n",
    "        else:\n",
    "            outcome = 'draw'\n",
    "        \n",
    "        games.append({\n",
    "            'iteration': iteration,\n",
    "            'game': game,\n",
    "            'p1_return': p1_return,\n",
    "            'p2_return': p2_return,\n",
    "            'outcome': outcome,\n",
    "            'final_step': tf_ttt_env.current_time_step()\n",
    "        })\n",
    "\n",
    "def train():\n",
    "    for _ in range(train_steps_per_iteration):\n",
    "        p1_train_info = player_1.train_iteration()\n",
    "        p2_train_info = player_2.train_iteration()\n",
    "    \n",
    "        loss_infos.append({\n",
    "            'iteration': iteration,\n",
    "            'p1_loss': p1_train_info.loss.numpy(),\n",
    "            'p2_loss': p2_train_info.loss.numpy()\n",
    "        })\n",
    "\n",
    "def plot_history():\n",
    "    \n",
    "    games_data = pd.DataFrame.from_records(games)\n",
    "    loss_data = pd.DataFrame.from_records(loss_infos)\n",
    "    loss_data['Player 1'] = np.log(loss_data.p1_loss)\n",
    "    loss_data['Player 2'] = np.log(loss_data.p2_loss)\n",
    "    \n",
    "    fig, axs = plt.subplots(2, 2, figsize=(15, 12))\n",
    "    \n",
    "    loss_melted = pd.melt(loss_data, \n",
    "                          id_vars=['iteration'], \n",
    "                          value_vars=['Player 1', 'Player 2'])\n",
    "    smoothing = iteration // 50\n",
    "    loss_melted.iteration = smoothing * (loss_melted.iteration // smoothing)\n",
    "\n",
    "    sns.lineplot(ax=axs[0][0], \n",
    "                 x='iteration', hue='variable', \n",
    "                 y='value', data=loss_melted)\n",
    "    axs[0][0].set_title('Loss History')\n",
    "    axs[0][0].set_ylabel('log-loss')\n",
    "    \n",
    "    returns_melted = pd.melt(games_data, \n",
    "                             id_vars=['iteration'], \n",
    "                             value_vars=['p1_return', 'p2_return'])\n",
    "    returns_melted.iteration = smoothing * (returns_melted.iteration // smoothing)\n",
    "    sns.lineplot(ax=axs[0][1], \n",
    "                 x='iteration', hue='variable', \n",
    "                 y='value', data=returns_melted)\n",
    "    axs[0][1].set_title('Return History')\n",
    "    axs[0][1].set_ylabel('return')\n",
    "\n",
    "    games_data['p1_win'] = games_data.outcome == 'p1_win'\n",
    "    games_data['p2_win'] = games_data.outcome == 'p2_win'\n",
    "    games_data['illegal'] = games_data.outcome == 'illegal'\n",
    "    grouped_games_data = games_data.groupby('iteration')\n",
    "    cols = ['game', 'p1_win', 'p2_win', 'illegal']\n",
    "    grouped_games_data = grouped_games_data[cols]\n",
    "    game_totals =  grouped_games_data.max()['game'] + 1\n",
    "    summed_games_data = grouped_games_data.sum()\n",
    "    summed_games_data['p1_win_rate'] = summed_games_data.p1_win / game_totals\n",
    "    summed_games_data['p2_win_rate'] = summed_games_data.p2_win / game_totals\n",
    "    summed_games_data['illegal_rate'] = summed_games_data.illegal / game_totals\n",
    "    summed_games_data['iteration'] = smoothing * (summed_games_data.index // smoothing)\n",
    "    \n",
    "    sns.lineplot(ax=axs[1][0],\n",
    "                 x='iteration',\n",
    "                 y='p1_win_rate',\n",
    "                 data=summed_games_data,\n",
    "                 label='Player 1 Win Rate')\n",
    "    sns.lineplot(ax=axs[1][0],\n",
    "                 x='iteration',\n",
    "                 y='p2_win_rate',\n",
    "                 data=summed_games_data,\n",
    "                 label='Player 2 Win Rate')\n",
    "    sns.lineplot(ax=axs[1][0],\n",
    "                 x='iteration',\n",
    "                 y='illegal_rate',\n",
    "                 data=summed_games_data,\n",
    "                 label='Illegal Ending Rate')\n",
    "    axs[1][0].set_title('Outcomes History')\n",
    "    axs[1][0].set_ylabel('ratio')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Training Set-up \n",
    "\n",
    "Here we are defining the parameters for the training run and building the agents. Most of these parameters are straight forward and don't require much consideration, but the `replay_buffer_size` is particular to the circumstances. Usually, in single-agent reinforcement learning, the environment is *stationary*, meaning that state-transitions and rewards that were observed in the past can be assumed to have stable distributions. However, in the IQL setting the expected return changes as the other agents in your environment change their strategies. This means that data stored in the replay buffer quickly becomes out-of-date, leading the training gradients of the loss function to not accurately reflect a step in the direction of improved performance at the present. As a result, if we have large replay buffers we see the loss function *increase*, rather than decrease, as we train. The TF-Agents `TFUniformReplayBuffer` implements a first in, first out (FIFO) mechanism, so if we restrict the maximum size to only store the trajectories for the past few iterations we will make sure that the environment appears approximately stationary from the perspective of the training step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_iterations = 2000\n",
    "initial_collect_episodes = 100\n",
    "episodes_per_iteration = 10\n",
    "train_steps_per_iteration = 1\n",
    "training_batch_size = 512\n",
    "training_num_steps = 2\n",
    "replay_buffer_size = 3 * episodes_per_iteration * 9\n",
    "learning_rate = 1e-2\n",
    "plot_interval = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"QNetwork\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "EncodingNetwork (EncodingNet multiple                  500       \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             multiple                  459       \n",
      "=================================================================\n",
      "Total params: 959\n",
      "Trainable params: 959\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"QNetwork\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "EncodingNetwork (EncodingNet multiple                  500       \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             multiple                  459       \n",
      "=================================================================\n",
      "Total params: 959\n",
      "Trainable params: 959\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Collecting Initial Training Sample...\n",
      "Samples collected\n"
     ]
    }
   ],
   "source": [
    "iteration = 1\n",
    "games = []\n",
    "loss_infos = []\n",
    "\n",
    "player_1 = IMAgent(\n",
    "    tf_ttt_env,\n",
    "    action_spec = tf_ttt_env.action_spec()['position'],\n",
    "    action_fn = partial(ttt_action_fn, 1),\n",
    "    name='Player1',\n",
    "    learning_rate = learning_rate,\n",
    "    training_batch_size = training_batch_size,\n",
    "    training_num_steps = training_num_steps,\n",
    "    replay_buffer_max_length = replay_buffer_size,\n",
    "    td_errors_loss_fn=common.element_wise_squared_loss\n",
    ")\n",
    "\n",
    "player_2 = IMAgent(\n",
    "    tf_ttt_env,\n",
    "    action_spec = tf_ttt_env.action_spec()['position'],\n",
    "    action_fn = partial(ttt_action_fn, 2),\n",
    "    reward_fn = p2_reward_fn,\n",
    "    name='Player2',\n",
    "    learning_rate = learning_rate,\n",
    "    training_batch_size = training_batch_size,\n",
    "    training_num_steps = training_num_steps,\n",
    "    replay_buffer_max_length = replay_buffer_size,\n",
    "    td_errors_loss_fn=common.element_wise_squared_loss\n",
    ")\n",
    "\n",
    "print('Collecting Initial Training Sample...')\n",
    "for _ in range(initial_collect_episodes):\n",
    "    training_episode(tf_ttt_env, player_1, player_2)\n",
    "print('Samples collected')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bb7b1b2e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    if iteration > 1:\n",
    "        plot_history()\n",
    "        clear_output(wait=True)\n",
    "    while iteration < num_iterations:\n",
    "        collect_training_data()\n",
    "        train()\n",
    "        iteration += 1\n",
    "        if iteration % plot_interval == 0:\n",
    "            plot_history()\n",
    "            clear_output(wait=True)\n",
    "\n",
    "except KeyboardInterrupt:\n",
    "    clear_output(wait=True)\n",
    "    print('Interrupting training, plotting history...')\n",
    "    plot_history()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the Agents\n",
    "\n",
    "Now that we have training the agents to play the game, we can see them play some games.\n",
    "\n",
    "First, lets see what happens when player 1 goes first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start:\n",
      "\n",
      "      |   |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: Player1, Action: 5, Reward: 0.0\n",
      "\n",
      "      |   |  \n",
      "    - + - + -\n",
      "      |   | X\n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: Player2, Action: 4, Reward: 0.0\n",
      "\n",
      "      |   |  \n",
      "    - + - + -\n",
      "      | O | X\n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: Player1, Action: 3, Reward: 0.0\n",
      "\n",
      "      |   |  \n",
      "    - + - + -\n",
      "    X | O | X\n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: Player2, Action: 2, Reward: 0.0\n",
      "\n",
      "      |   | O\n",
      "    - + - + -\n",
      "    X | O | X\n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: Player1, Action: 0, Reward: 0.0\n",
      "\n",
      "    X |   | O\n",
      "    - + - + -\n",
      "    X | O | X\n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: Player2, Action: 7, Reward: 0.0\n",
      "\n",
      "    X |   | O\n",
      "    - + - + -\n",
      "    X | O | X\n",
      "    - + - + -\n",
      "      | O |  \n",
      "    \n",
      "Player: Player1, Action: 6, Reward: 1.0\n",
      "\n",
      "    X |   | O\n",
      "    - + - + -\n",
      "    X | O | X\n",
      "    - + - + -\n",
      "    X | O |  \n",
      "    \n"
     ]
    }
   ],
   "source": [
    "ts = tf_ttt_env.reset()\n",
    "player_1.reset()\n",
    "player_2.reset()\n",
    "print('Start:')\n",
    "print_tic_tac_toe(ts.observation.numpy())\n",
    "\n",
    "players = cycle([player_1, player_2])\n",
    "while not ts.is_last():\n",
    "    player = next(players)\n",
    "    traj = player.act()\n",
    "    ts = player._current_time_step()\n",
    "    act = traj.action[0]\n",
    "    print(f'Player: {player.name}, '\n",
    "          f'Action: {(act % 3, act // 3)}, '\n",
    "          f'Reward: {ts.reward.numpy()[0]}')\n",
    "    print_tic_tac_toe(ts.observation.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets see what happens when player 2 goes first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start:\n",
      "\n",
      "      |   |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: Player2, Action: 3, Reward: 0.0\n",
      "\n",
      "      |   |  \n",
      "    - + - + -\n",
      "    O |   |  \n",
      "    - + - + -\n",
      "      |   |  \n",
      "    \n",
      "Player: Player1, Action: 8, Reward: 0.0\n",
      "\n",
      "      |   |  \n",
      "    - + - + -\n",
      "    O |   |  \n",
      "    - + - + -\n",
      "      |   | X\n",
      "    \n",
      "Player: Player2, Action: 4, Reward: 0.0\n",
      "\n",
      "      |   |  \n",
      "    - + - + -\n",
      "    O | O |  \n",
      "    - + - + -\n",
      "      |   | X\n",
      "    \n",
      "Player: Player1, Action: 2, Reward: 0.0\n",
      "\n",
      "      |   | X\n",
      "    - + - + -\n",
      "    O | O |  \n",
      "    - + - + -\n",
      "      |   | X\n",
      "    \n",
      "Player: Player2, Action: 0, Reward: 0.0\n",
      "\n",
      "    O |   | X\n",
      "    - + - + -\n",
      "    O | O |  \n",
      "    - + - + -\n",
      "      |   | X\n",
      "    \n",
      "Player: Player1, Action: 6, Reward: 0.0\n",
      "\n",
      "    O |   | X\n",
      "    - + - + -\n",
      "    O | O |  \n",
      "    - + - + -\n",
      "    X |   | X\n",
      "    \n",
      "Player: Player2, Action: 7, Reward: 0.0\n",
      "\n",
      "    O |   | X\n",
      "    - + - + -\n",
      "    O | O |  \n",
      "    - + - + -\n",
      "    X | O | X\n",
      "    \n",
      "Player: Player1, Action: 1, Reward: 0.0\n",
      "\n",
      "    O | X | X\n",
      "    - + - + -\n",
      "    O | O |  \n",
      "    - + - + -\n",
      "    X | O | X\n",
      "    \n",
      "Player: Player2, Action: 5, Reward: 1.0\n",
      "\n",
      "    O | X | X\n",
      "    - + - + -\n",
      "    O | O | O\n",
      "    - + - + -\n",
      "    X | O | X\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "ts = tf_ttt_env.reset()\n",
    "player_1.reset()\n",
    "player_2.reset()\n",
    "print('Start:')\n",
    "print_tic_tac_toe(ts.observation.numpy())\n",
    "\n",
    "players = cycle([player_2, player_1])\n",
    "while not ts.is_last():\n",
    "    player = next(players)\n",
    "    traj = player.act()\n",
    "    ts = player._current_time_step()\n",
    "    act = traj.action[0]\n",
    "    print(f'Player: {player.name}, '\n",
    "          f'Action: {(act % 3, act // 3)}, '\n",
    "          f'Reward: {ts.reward.numpy()[0]}')\n",
    "    print_tic_tac_toe(ts.observation.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the agents have (mostly) learned to avoid illegal moves, however, they are still missing obvious opportunties to win the game or block their opponent. Further training and/or changing the agent architectures would likely improve their play. \n",
    "\n",
    "\n",
    "## Conclusions\n",
    "\n",
    "In this tutorial we have implemented a class for intrinsically motivated DQN agents using TF-Agents, and we have instantiated multiple agents to act as independent Q-learners in a Tic Tac Toe environment. This framework is general a should be easily adaptable to different agent types, more agents, and different environments. "
   ]
  }
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