From 09701f70bed858f51e927ef4a63a6f5345a370b9 Mon Sep 17 00:00:00 2001
From: bd-912 <bdunahu@gmail.com>
Date: Tue, 12 Dec 2023 00:18:30 -0700
Subject: Credited Dr. Anderson for his contribution, renamed notebook files

---
 QNetwork/neuralnetwork_regression.py |   8 +-
 QNetwork/optimizers.py               |   8 +-
 README.org                           |   3 +
 one_revised_snake_q_table.ipynb      | 755 +++++++++++++++++++++++++++++++++++
 revised_snake_q_network.ipynb        | 597 ---------------------------
 revised_snake_q_table.ipynb          | 745 ----------------------------------
 two_revised_snake_q_network.ipynb    | 603 ++++++++++++++++++++++++++++
 7 files changed, 1371 insertions(+), 1348 deletions(-)
 create mode 100644 README.org
 create mode 100644 one_revised_snake_q_table.ipynb
 delete mode 100644 revised_snake_q_network.ipynb
 delete mode 100644 revised_snake_q_table.ipynb
 create mode 100644 two_revised_snake_q_network.ipynb

diff --git a/QNetwork/neuralnetwork_regression.py b/QNetwork/neuralnetwork_regression.py
index b26be5e..3f141c5 100644
--- a/QNetwork/neuralnetwork_regression.py
+++ b/QNetwork/neuralnetwork_regression.py
@@ -6,9 +6,11 @@ import matplotlib.patches as pltpatch  # for Arc
 import matplotlib.collections as pltcoll
 import math
 
-######################################################################
-## class NeuralNetwork()
-######################################################################
+###############################################
+# Thanks to Dr. Chuck Anderson for his neural #
+# network with optimizers implementation      #
+# https://www.cs.colostate.edu/~anderson/wp/  #
+###############################################
 
 class NeuralNetwork():
 
diff --git a/QNetwork/optimizers.py b/QNetwork/optimizers.py
index 7d28f92..c928b64 100644
--- a/QNetwork/optimizers.py
+++ b/QNetwork/optimizers.py
@@ -1,8 +1,10 @@
 import numpy as np
 
-######################################################################
-## class Optimizers()
-######################################################################
+###############################################
+# Thanks to Dr. Chuck Anderson for his neural #
+# network with optimizers implementation      #
+# https://www.cs.colostate.edu/~anderson/wp/  #
+###############################################
 
 class Optimizers():
 
diff --git a/README.org b/README.org
new file mode 100644
index 0000000..64ebf54
--- /dev/null
+++ b/README.org
@@ -0,0 +1,3 @@
+* README
+
+(work in progress)
diff --git a/one_revised_snake_q_table.ipynb b/one_revised_snake_q_table.ipynb
new file mode 100644
index 0000000..9a6ca5f
--- /dev/null
+++ b/one_revised_snake_q_table.ipynb
@@ -0,0 +1,755 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "85da5df2-c926-417c-bd7b-d214ad31ebe1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pygame 2.5.1 (SDL 2.28.2, Python 3.11.5)\n",
+      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from collections import namedtuple\n",
+    "from IPython.core.debugger import Pdb\n",
+    "\n",
+    "from GameEngine import multiplayer\n",
+    "Point = namedtuple('Point', 'x, y')\n",
+    "SA = namedtuple('SA', 'state, action')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d264ae4a-380c-47b6-9b29-c6fe16c6399c",
+   "metadata": {},
+   "source": [
+    "### New Game Implementation\n",
+    "\n",
+    "I have an improved game implementation which allows for multiplayer snake games, as well as simplified training. This notebook will go over both of these, including an implementation of q-table learning, as well as a match between a manually filled out q-table and a learned one.\n",
+    "\n",
+    "I'll start by initializing the engine object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2a382240-906d-474f-94c0-9af1a5de97ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# defines game window size and block size, in pixels\n",
+    "WINDOW_WIDTH = 640\n",
+    "WINDOW_HEIGHT = 480\n",
+    "GAME_UNITS = 80"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "976eff80-c50a-492e-a49b-975d9905e274",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "game_engine = multiplayer.Playfield(window_width=WINDOW_WIDTH,\n",
+    "                                    window_height=WINDOW_HEIGHT,\n",
+    "                                    units=GAME_UNITS,\n",
+    "                                    g_speed=35,\n",
+    "                                    s_size=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23de2fca-d31a-497f-9a0c-845c568e5df7",
+   "metadata": {},
+   "source": [
+    "Here is a run-down of the current functions available to programs utilizing the game engine:\n",
+    "\n",
+    "**add_player**: Returns the player's number to the callee (between 0-3, for a total of 4 players). This number can be used with other functions to index that player's state.\n",
+    "\n",
+    "**get_heads_tails_and_goal**: Returns an array of player heads (in order of player number) the locations of all snake tails, as well as the goal location. Each is stored in an array of named tuples.\n",
+    "\n",
+    "**get_viable_actions**: Given a player's id, returns a list of integers corresponding to actions which will not immediately result in the snake's death.\n",
+    "0 = UP\n",
+    "1 = RIGHT\n",
+    "2 = DOWN\n",
+    "3 = LEFT\n",
+    "\n",
+    "**start_game**: Initializes goal, player, score, and playfield objects. Disables the ability to add new players. Enables use of player_advance function.\n",
+    "\n",
+    "**stop_game**: Sets the game_state to false, allowing new players to be added.\n",
+    "\n",
+    "**cleanup**: Quits the pygame window.\n",
+    "\n",
+    "**player_advance**: Given an array corresponding to each player's action (integers), returns a list of collision results, and updates the internal game state.\n",
+    "\n",
+    "**toggle_draw**: Turns off/on the game UI for faster training.\n",
+    "\n",
+    "I can do a test of these functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "64470c1a-ddc6-4ce1-b6d4-f1e17e3d0176",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Game starting with 1 players.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p1 = game_engine.add_player()\n",
+    "game_engine.start_game()\n",
+    "p1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "28d7fa43-4419-4940-9d4f-d6bd5ccc11ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "viable_actions = game_engine.get_viable_actions(p1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ba45b03a-9c42-48e9-a259-5d01e667157d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<CollisionType.NONE: 2>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "game_engine.player_advance([np.random.choice(viable_actions)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df7170ae-e95d-404b-b632-e8e9de46d69e",
+   "metadata": {},
+   "source": [
+    "If you looked at the UI for this last statement, you should have seen that the game moved the snake (yellow) in a random direction away from immediate death."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "144bc5ff-756c-4e07-a855-4020a4474d52",
+   "metadata": {},
+   "source": [
+    "### State-sensing methods, creating and reading a q-table\n",
+    "Now, we can start redesigning some functions used to allow the snake to play intelligently. We'll use a multi-dimensional numpy array to store the rewards corresponding to each state and action. This is called a q-function, or a q-table in this case.\n",
+    "\n",
+    "How many states do I need? Seeing how the new **get_viable_actions** method already prevents the snake from choosing life-ending moves, the snake is no longer tasked with learning or memorizing it.\n",
+    "\n",
+    "The snake does need to be able to interpret progress towards the goal, so I will reinclude one state; the compass direction from the snake's head to the goal. This means I need only 8 states (with entries for each four actions) to represent my game now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "88a69876-fbc7-4dc1-a471-d8449fada4e4",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goal_relations = 8\n",
+    "actions = 4\n",
+    "q = np.zeros((goal_relations,\n",
+    "              actions))\n",
+    "q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3e15744e-1251-4315-8a4c-ed5788d04478",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sense_goal(head, goal):\n",
+    "    '''\n",
+    "    maps head and goal location onto an\n",
+    "    integer corresponding to approx location\n",
+    "    '''\n",
+    "    diffs = Point(goal.x - head.x, goal.y - head.y)\n",
+    "\n",
+    "    if diffs.x == 0 and diffs.y <  0:\n",
+    "        return 0\n",
+    "    if diffs.x >  0 and diffs.y <  0:\n",
+    "        return 1\n",
+    "    if diffs.x >  0 and diffs.y == 0:\n",
+    "        return 2\n",
+    "    if diffs.x >  0 and diffs.y >  0:\n",
+    "        return 3\n",
+    "    if diffs.x == 0 and diffs.y >  0:\n",
+    "        return 4\n",
+    "    if diffs.x <  0 and diffs.y >  0:\n",
+    "        return 5\n",
+    "    if diffs.x <  0 and diffs.y == 0:\n",
+    "        return 6\n",
+    "    return 7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "addf716b-892c-4f7f-b71f-c6af8779dff7",
+   "metadata": {},
+   "source": [
+    "I will use the getter provided by my engine, which queries various statistics about all agents in the game:\n",
+    "1. An array of head positions\n",
+    "2. An array of all tail locations\n",
+    "3. The goal location"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7b5f0d57-8e26-4f95-b7ea-a6810936ad5d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([Point(x=320, y=160)], [Point(x=320, y=160)], Point(x=0, y=400))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "game_engine.get_heads_tails_and_goal()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3fd47ce-55fe-4d2f-9147-8848193f7ca1",
+   "metadata": {},
+   "source": [
+    "Now to make a function to index our expected reward-to-go given a state using sense_goal:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "85e2bab1-c98b-400e-be41-a47ccd4bc163",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def index_actions(q, id):\n",
+    "    '''\n",
+    "    given q, player_id, an array of heads,\n",
+    "    and the goal position,\n",
+    "    indexes into the corresponding expected\n",
+    "    reward of each action\n",
+    "    '''\n",
+    "    heads, tails, goal = game_engine.get_heads_tails_and_goal()\n",
+    "    state = sense_goal(heads[id], goal)\n",
+    "    return state, q[state, :]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33ae53a8-989a-410c-a04f-2ac76561ce21",
+   "metadata": {},
+   "source": [
+    "Returning state here simplifies some logic later when I train the agent. It will be passed along to my next function, but it can be ignored for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3808c200-2f67-43c4-a80f-c4c17dcfeacf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_, rewards = index_actions(q, p1)\n",
+    "rewards"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a2ef7f7-f6f7-4610-8e98-1d389327f3e8",
+   "metadata": {},
+   "source": [
+    "In our learning agent, these actions will obviously be associated with different expected rewards. Essentially, we have a function that, given a state, tells us the expected utility of each action. Should we just choose the best one?\n",
+    "\n",
+    "There are two problems with a greedy approach...\n",
+    "\n",
+    "1. If the agent only sticks to the one policy it knows, then it will never truly learn the best solution. This is not the largest problem, due to how few states we have. Additionally, the snake is forced into a new state everytime it collects a goal. Still, a pure greedy approach results in slow, sub-optimal training.\n",
+    "2. My snake is ignorant of which actions will result in a collision. The snake must be able to understand that its Q function will commonly lead it astray in navigating its environment.\n",
+    "\n",
+    "In cases where the snake has explored enough to make its Q function useful and death is not a possibility, I still do want the snake to be greedy. I chose to implement a replacement argmin/max function to select actions from this table, which generates new actions in order from highest expected reward to lowest expected reward."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a172e347-75b7-4b0a-8dcc-07a6ba04f77e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def argmin_gen(rewards):\n",
+    "    rewards = rewards.copy()\n",
+    "    for i in range(rewards.size):\n",
+    "        best_action = np.argmin(rewards)\n",
+    "        rewards[best_action] = float(\"inf\")\n",
+    "        yield best_action"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9674f225-e7df-4551-baa8-29eb23fcc1d5",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "for action in argmin_gen(rewards):\n",
+    "    print(action)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9bed101-661c-4e61-b8ad-94bbc1900e03",
+   "metadata": {},
+   "source": [
+    "How will I use this? Remember that my game engine includes all the logic to calculate which actions are immediately dangerous. If the action generated is not a viable action, we will take the next best action, or the next, next best action, etc.\n",
+    "\n",
+    "What if no actions are viable? Then the agent has boxed itself in, and it doesn't matter what action we choose.\n",
+    "\n",
+    "Previously, I reset the snake if it got stuck in a learning-loop. I will instead use epsilon, as it is the tried-and-true method of enforcing exploration.\n",
+    "\n",
+    "Here is my greedy-epsilon function, combining the work of all the previous code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d3d65397-62a9-47b6-9c84-808282656f80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pick_greedy_action(q, id, epsilon):\n",
+    "    viable_actions = game_engine.get_viable_actions(id)\n",
+    "    state, rewards = index_actions(q, id)\n",
+    "\n",
+    "    if np.random.uniform() < epsilon:\n",
+    "        # SA -- a STATE-ACTION pair (X as in function input)\n",
+    "        return SA(state, np.random.choice(viable_actions)) if viable_actions.size > 0 else SA(state, 0)\n",
+    "    for action in argmin_gen(rewards):\n",
+    "        if action in viable_actions:\n",
+    "            return SA(state, action)\n",
+    "    return SA(state, 0) # death"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2169ac2b-df83-4f53-8a83-b35a2d1a521a",
+   "metadata": {},
+   "source": [
+    "I'll set up epsilon to decay over our 500-step test..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a5932d47-adbe-46de-b91e-582e40faf369",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_steps = 200\n",
+    "epsilon = 1\n",
+    "final_epsilon = 0.001\n",
+    "epsilon_decay =  np.exp(np.log(final_epsilon) / (n_steps))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee7b066a-330d-4cdd-bbb2-9d2a7ad2ceb4",
+   "metadata": {},
+   "source": [
+    "And watch the snake explore:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6beff583-e32a-4c15-8fcb-f5b5d45ad548",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for step in range(n_steps):\n",
+    "    _, p1_action = pick_greedy_action(q, p1, epsilon)\n",
+    "    game_engine.player_advance([p1_action])\n",
+    "    epsilon *= epsilon_decay"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ee2c2e6-933d-460e-b2fc-f5bf1f22e381",
+   "metadata": {},
+   "source": [
+    "This snake obviously has no prior knowledge of how to earn the most reward, but it still does remarkably well because it is not allowed to die. It behaves as expected, favoring up and right when it is not forced to choose a random action.\n",
+    "\n",
+    "Our q_table only has 32 values as a result of removing the 16 danger states... It would be incredibly easy to manually select reward values to fill our q_table with..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "7af51359-872c-4d1b-b178-e27cf86eb3cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "set_q = np.array([[-10., -2., 0., -2.],\n",
+    "                  [-5., -5., 0., 0.],\n",
+    "                  [-2., -10., 2., 0.],\n",
+    "                  [0., -5., -5., 0.],\n",
+    "                  [0., -2., -10., -2.],\n",
+    "                  [0., 0., -5., -5.],\n",
+    "                  [-2., 0., -2., -10.],\n",
+    "                  [-5., 0., 0., -5.]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4da4d318-f7e0-412b-8545-8fd346e167b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epsilon = 0\n",
+    "for step in range(n_steps):\n",
+    "    X = pick_greedy_action(set_q, p1, epsilon)\n",
+    "    game_engine.player_advance([X.action])\n",
+    "    epsilon *= epsilon_decay"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c36ab97-2ca0-4468-8d4c-ebd1e4deec23",
+   "metadata": {},
+   "source": [
+    "And the snake already plays optimally, no learning required.\n",
+    "\n",
+    "Now that we have these methods, I will create functions to allow the snake to learn by its own, and then pair it off against the q-table I just built."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b9968af-0ec2-4b92-a19d-50912703dd4a",
+   "metadata": {},
+   "source": [
+    "### Q-Learning, with Temporal Difference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce537e44-ac8c-4f09-b89d-a330f13277da",
+   "metadata": {},
+   "source": [
+    "A good agent prioritizes actions that leads to the highest expected reward. The Q-function assigns an expected utility to each state-action pair, usually the expected reward-to-go.\n",
+    "\n",
+    "A popular method of adjusting this state-value function is a version of the temporal difference equation, which adjusts the utility associated with each input to agree with the maximum utility of its successor:\n",
+    "\n",
+    "$Q(s,a) = Q(s,a) + \\alpha[(R(s,a,s') + \\gamma * max _a'Q(s',a') - Q(s,a))]$\n",
+    "\n",
+    "The nature of this method is somewhat recursive, as it updates Q to agree with max(Q'), which in turn is updated to agree with max(Q'')... which is why the algorithm is sometimes referred to as bootstrapping.\n",
+    "\n",
+    "The discount factor $\\gamma$ can be used to weight immediate reward higher than future reward, though will be kept as 1 in my solution, which means we consider all future actions equally. All I need to do is assign an enticing enough reinforcement to goal-attaining actions, and use the temporal difference equation to update all other state transitions.\n",
+    "\n",
+    "The complication is that we require two time states to peMy below function takes the I simply need to create a function that takes the current state/action/outcome, and the previous state/action, as this will be updated in cases where the agent did not reach the goal.\n",
+    "\n",
+    "When the agent does reach the goal, I will manually set that state and action to the best reward, 0. Remember that the q-table is initialized with zeros, meaning untravelled actions are pre-assigned good rewards. Both this and epsilon will encourage exploration.\n",
+    "\n",
+    "Here is the complete function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a0d3942a-af6a-41f3-be74-167e3abaae0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reward = -1\n",
+    "\n",
+    "def update_q(q, old_X, new_X, outcome, lr=0.05):\n",
+    "    if outcome == multiplayer.CollisionType.GOAL:\n",
+    "        q[new_X.state, new_X.action] = 0\n",
+    "    else:\n",
+    "        td_error = reward + q[new_X.state, new_X.action] - q[old_X.state, old_X.action]\n",
+    "        q[old_X.state, old_X.action] += lr * td_error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01b21e01-174e-4fdd-ad70-dcc1e6483fb2",
+   "metadata": {},
+   "source": [
+    "Now all that is needed is the training loop. I have high expectations for this agent, so I will only allow it 1500 moves to train itself! Here is where the outputs of pick_greedy_action come in handy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "95a2ec72-e30c-4730-a876-21f054d3727f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_steps = 1500\n",
+    "epsilon = 1\n",
+    "final_epsilon = 0.001\n",
+    "epsilon_decay =  np.exp(np.log(final_epsilon) / (n_steps))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a71dc022-5e51-46f8-bfed-37b95243fc5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p1_old_X = pick_greedy_action(q, p1, epsilon) # state, action\n",
+    "game_engine.player_advance([p1_old_X.action])\n",
+    "\n",
+    "for step in range(n_steps):\n",
+    "    p1_new_X = pick_greedy_action(q, p1, epsilon) # state, action\n",
+    "    outcome = game_engine.player_advance([p1_new_X.action])\n",
+    "\n",
+    "    update_q(q, p1_old_X, p1_new_X, outcome)\n",
+    "\n",
+    "    epsilon *= epsilon_decay\n",
+    "    p1_old_X = p1_new_X"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6cbf429-c790-4bb9-8775-ed067844ab4e",
+   "metadata": {},
+   "source": [
+    "Most of the time, results look promising. Here is everything it learned:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ad12d31a-a1ec-45af-89b0-f66ca375b524",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-2.35070513, -1.09149338, -0.16535442, -0.56686264],\n",
+       "       [-9.89365379, -3.92409428, -5.34681609, -1.89000207],\n",
+       "       [-0.11260587, -4.74939046, -9.9192565 , -0.43756285],\n",
+       "       [-1.40606955, -3.09973297, -1.00213181, -0.59918486],\n",
+       "       [ 0.        , -0.48427898, -3.18961465, -0.15631095],\n",
+       "       [-1.23097846, -1.83318991, -3.93160124, -1.29734622],\n",
+       "       [-0.85699409, -0.19789849, -0.82954074, -5.23579225],\n",
+       "       [-4.3355246 , -1.17885939, -0.53698344, -2.4048526 ]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2414728-c36c-45d6-8f2d-18e78e482054",
+   "metadata": {},
+   "source": [
+    "### Multiplayer Demonstration, Saving Tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24c73f20-2853-4ba7-a24e-22c5a4b8da5e",
+   "metadata": {},
+   "source": [
+    "The most entertaining way to test the success of my implementation is pair the agents q and set_q against each other. I will first stop and set up a new game:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a93f0fe5-2bc4-45d7-ba31-2306efaa9806",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Game over!\n",
+      "Game starting with 2 players.\n"
+     ]
+    }
+   ],
+   "source": [
+    "game_engine.stop_game()\n",
+    "p2 = game_engine.add_player()\n",
+    "game_engine.start_game()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47a1adde-d95d-444e-9688-1290764f8cfa",
+   "metadata": {},
+   "source": [
+    "Now, I can simply call player advance with both player's actions in order, and the engine will handle the rest. I will define a new game loop, similar to the previous one:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d5b5d089-cb66-47de-b352-36c13fd7dead",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epsilon = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "4de326e6-82dc-48df-8920-d28d0154fbd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for step in range(n_steps):\n",
+    "    # p1\n",
+    "    p1_X = pick_greedy_action(set_q, p1, epsilon)\n",
+    "\n",
+    "    # p2\n",
+    "    p2_X = pick_greedy_action(q, p2, epsilon) # state, action\n",
+    "    \n",
+    "    game_engine.player_advance([p1_X.action, p2_X.action])\n",
+    "\n",
+    "    epsilon *= epsilon_decay"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "820d7e5f-c3e9-4dae-82ce-3c9188d8a8d8",
+   "metadata": {},
+   "source": [
+    "The learned agent usually plays almost as well as the artificially-learned one, which is okay given I hardly spent time optimizing the number of steps and learning rate. I plan to compare both of the agents again my neural-network approach, so the last I will do is save the q_tables to a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "95227c89-e7db-4923-9160-dacfa1cf4af8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.save('superior_qt.npy', set_q)\n",
+    "np.save('inferior_qt.npy', q)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/revised_snake_q_network.ipynb b/revised_snake_q_network.ipynb
deleted file mode 100644
index fbab70d..0000000
--- a/revised_snake_q_network.ipynb
+++ /dev/null
@@ -1,597 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "73c6d255-0c32-4895-9a22-e95eadb25103",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pygame 2.5.1 (SDL 2.28.2, Python 3.11.5)\n",
-      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from collections import namedtuple\n",
-    "from IPython.core.debugger import Pdb\n",
-    "from IPython.display import display, clear_output\n",
-    "\n",
-    "from QNetwork import neuralnetwork_regression as nn\n",
-    "from GameEngine import multiplayer\n",
-    "from QTable import qtsnake\n",
-    "\n",
-    "Point = namedtuple('Point', 'x, y')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b3aab739-e016-4700-89c9-41f3c2f536cf",
-   "metadata": {},
-   "source": [
-    "### New Game Implementation\n",
-    "\n",
-    "I have an improved game implementation which allows for multiplayer snake games, as well as simplified training. This notebook will go over training of a simple q-network, which maps a total of 32 different combinations of states and actions onto rewards, much like the previous q-table implementation from ***revised_snake_q_table.ipynb***.\n",
-    "\n",
-    "Please read that notebook first if interested in a more complete description of the new game engine. As usual, we have some game-setup to do:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "682a7036-4f0d-4f3d-b147-6355c0a2f93e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# defines game window size and block size, in pixels\n",
-    "WINDOW_WIDTH = 640\n",
-    "WINDOW_HEIGHT = 480\n",
-    "GAME_UNITS = 80"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "41cfbec9-e14e-4c58-95dd-2e3fb1788e72",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "game_engine = multiplayer.Playfield(window_width=WINDOW_WIDTH,\n",
-    "                                    window_height=WINDOW_HEIGHT,\n",
-    "                                    units=GAME_UNITS,\n",
-    "                                    g_speed=35,\n",
-    "                                    s_size=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "804a13dc-7dd4-43f0-bc47-e781bc022075",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Game starting with 1 players.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p1 = game_engine.add_player()\n",
-    "game_engine.start_game()\n",
-    "p1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34efdb66-7a8e-4b48-a015-d1eb8a029915",
-   "metadata": {},
-   "source": [
-    "Training thousands of steps is a little bit slow with the graphics on. It makes only a small difference here, but it provides little information anyways:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "b94f16d4-65bb-4150-bdc0-6cc648e3cb7e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Draw is now False.\n"
-     ]
-    }
-   ],
-   "source": [
-    "game_engine.toggle_draw()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43cefedf-e005-4910-9b4c-953697aa3f26",
-   "metadata": {},
-   "source": [
-    "### State-sensing methods, defining reinforcement and greedy-action selector\n",
-    "\n",
-    "I have also imported the aforementioned q_table implementation as qtsnake. It will come back in the end of the notebook when I pair the q_table and q_network against each other, but to make the game fair, I'll use the exact same state-sensing method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "71c97804-74d3-4248-bdb7-5519aa02b556",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function QTable.qtsnake.sense_goal(head, goal)>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "qtsnake.sense_goal"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e065f223-9e19-4f21-ba75-8d44fc62d353",
-   "metadata": {},
-   "source": [
-    "Even though I plan to only call it when selecting a greedy_action, I'll wrap it in a neat 'query_state' function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "26b8f8bf-ad08-40f8-847f-88351e262c1d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def query_state(id):\n",
-    "    '''\n",
-    "    given a player's id,\n",
-    "    returns their state\n",
-    "    '''\n",
-    "    heads, _, goal = game_engine.get_heads_tails_and_goal()\n",
-    "    return np.array(qtsnake.sense_goal(heads[id], goal))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d61e508-0661-4893-a720-f0a511c52809",
-   "metadata": {},
-   "source": [
-    "And a reinforcement function. Because I took the requirement to sense danger away, we only need two outputs from the reinforcement function.\n",
-    "\n",
-    "The output of this function was chosen due to being the best-performing. It is possible the reward for GOAL should be higher or lower. In actuality, the reinforcement for non-goals will never be used. I prefer the simplicity of using the discount factor to force agents to the goal quickly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "0af0a115-83b9-498a-8228-dc79580131f1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def reinforcement(outcome):\n",
-    "    '''\n",
-    "    given an outcome of an action,\n",
-    "    returns associated reward\n",
-    "    '''\n",
-    "    if outcome == multiplayer.CollisionType.GOAL:\n",
-    "        return -3\n",
-    "    return 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45e6040c-9aae-4f9e-8ef6-cf23b4043622",
-   "metadata": {},
-   "source": [
-    "Here is the first real interesting function. It takes its implementation largely from the marble example, but it accepts and returns parameters as closely to the previous q-table version.\n",
-    "\n",
-    "In essence, I ask the game the viable actions for a player, take into account our current state, and choose the action with the greatest expected reward, or a random action. This is called epsilon greedy selection.\n",
-    "\n",
-    "When calling use on the network, it maps a state and action onto a reward, just the same as indexing the q-table. We return the expected reward for this action in addition, because it is needed later for learning with discounted rewards."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "a76fd63a-478a-43ad-91ce-df1dff03e565",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def pick_greedy_action(q_net, id, epsilon):\n",
-    "    '''\n",
-    "    given a q network, the id of the player\n",
-    "    taking action, and a randomization factor,\n",
-    "    returns the most rewarding non-lethal action\n",
-    "    or a non-lethal random action and expected reward\n",
-    "    '''\n",
-    "    viable_actions = game_engine.get_viable_actions(id)\n",
-    "    state = query_state(id)\n",
-    "\n",
-    "    if viable_actions.size < 1:\n",
-    "        best_action = 0\n",
-    "    elif np.random.uniform() < epsilon:\n",
-    "        best_action = np.random.choice(viable_actions)\n",
-    "    else:\n",
-    "        qs = [q_net.use(np.hstack(\n",
-    "            (state, action)).reshape((1, -1))) for action in viable_actions]\n",
-    "        best_action = viable_actions[np.argmin(qs)]\n",
-    "\n",
-    "    X = np.hstack((state, best_action))\n",
-    "    q = q_net.use(X.reshape((1, -1)))\n",
-    "\n",
-    "    return X, q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "06cd085e-77f4-4a22-9b1f-ec364b7737c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_q(q, old_X, new_X, new_q, outcome, n_epochs, discount=0.9, lr=0.2):\n",
-    "    '''\n",
-    "    given a q network, the previous state/action pair,\n",
-    "    the new state/action pair, the expected next reward,\n",
-    "    the outcome of the last action, the number of epochs,\n",
-    "    a discount factor (gamma), and the learning rate\n",
-    "    updates q with discounted rewards.\n",
-    "    '''\n",
-    "    reward = reinforcement(outcome)\n",
-    "    if outcome == multiplayer.CollisionType.GOAL:\n",
-    "        q.train(np.array([new_X]),\n",
-    "                np.array([reward]) + np.array([[reward]]),\n",
-    "                n_epochs, lr, method='sgd', verbose=False)\n",
-    "    else:\n",
-    "        q.train(np.array([old_X]),\n",
-    "                discount * np.array([new_q]), n_epochs,\n",
-    "                lr, method='sgd', verbose=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "f51c3238-c918-40a5-bf38-1456f4ed4ff5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gamma = 0.9\n",
-    "n_epochs = 10\n",
-    "learning_rate = 0.015\n",
-    "\n",
-    "hidden_layers = [15]\n",
-    "q = nn.NeuralNetwork(2, hidden_layers, 1)\n",
-    "q.setup_standardization([5, 3.5], [4, np.sqrt(5.25)], [-.1], [0.2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "072ef9b7-86ec-4cbf-a315-dd6b4019fce6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_steps = 10000\n",
-    "epsilon = 1\n",
-    "final_epsilon = 0.05\n",
-    "epsilon_decay =  np.exp(np.log(final_epsilon) / (n_steps))\n",
-    "epsilon_trace = np.zeros(n_steps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "720a04aa-b53f-42d7-adf8-7c1a0958ff04",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Scoreboard():\n",
-    "    ''' tracks game statistics '''\n",
-    "    def __init__(self):\n",
-    "        self.all_goals = 0\n",
-    "        self._deaths = 0\n",
-    "        self._goals = 0\n",
-    "        self._max_goals = 0\n",
-    "\n",
-    "        self.goals = []\n",
-    "        self.deaths = []\n",
-    "        self.max_goals = []\n",
-    "\n",
-    "    def track_outcome(self, outcome):\n",
-    "        if outcome == multiplayer.CollisionType.GOAL:\n",
-    "            self._goals += 1\n",
-    "            self.all_goals += 1\n",
-    "            if self._goals > self._max_goals:\n",
-    "                self._max_goals = self._goals\n",
-    "        elif outcome == multiplayer.CollisionType.DEATH:\n",
-    "            self._deaths += 1\n",
-    "            self._goals = 0\n",
-    "\n",
-    "    def flush(self):\n",
-    "        self.goals.append(self._goals)\n",
-    "        self.deaths.append(self._deaths)\n",
-    "        self.max_goals.append(self._max_goals)\n",
-    "\n",
-    "        self._reset()\n",
-    "\n",
-    "    def _reset(self):\n",
-    "        self._deaths = 0\n",
-    "        self._goals = 0\n",
-    "        self._max_goals = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "c86cea77-c3b9-44fa-becd-2d04d49b92cc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_status(q, step, epsilon_trace, r_trace):\n",
-    "    \n",
-    "    plt.subplot(4, 3, 1)\n",
-    "    plt.plot(epsilon_trace[:step + 1])\n",
-    "    plt.ylabel('Random Action Probability ($\\epsilon$)')\n",
-    "    plt.ylim(0, 1)\n",
-    "\n",
-    "    plt.subplot(4, 3, 2)\n",
-    "    plt.plot(scoreboard.deaths)\n",
-    "    plt.ylabel('Deaths')\n",
-    "\n",
-    "    plt.subplot(4, 3, 3)\n",
-    "    plt.plot(scoreboard.goals)\n",
-    "    plt.ylabel('Goals')\n",
-    "\n",
-    "    plt.subplot(4, 3, 4)\n",
-    "    plt.plot(scoreboard.max_goals)\n",
-    "    plt.ylabel('Max Score')\n",
-    "\n",
-    "    '''\n",
-    "    plt.subplot(4, 3, 5)\n",
-    "    plt.plot(r_trace[:step + 1], alpha=0.5)\n",
-    "    binSize = 20\n",
-    "    if step+1 > binSize:\n",
-    "        # Calculate mean of every bin of binSize reinforcement values\n",
-    "        smoothed = np.mean(r_trace[:int(step / binSize) * binSize].reshape((int(step / binSize), binSize)), axis=1)\n",
-    "        plt.plot(np.arange(1, 1 + int(step / binSize)) * binSize, smoothed)\n",
-    "    plt.ylabel('Mean reinforcement')\n",
-    "    '''\n",
-    "\n",
-    "    plt.subplot(4, 3, 6)\n",
-    "    q.draw(['$o$', '$a$'], ['q'])\n",
-    "\n",
-    "    plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "00ca3585-8a11-4fd5-93d7-8e73bfc31e81",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "old_X, old_q = pick_greedy_action(q, p1, epsilon)\n",
-    "game_engine.player_advance([old_X[1]])\n",
-    "\n",
-    "fig = plt.figure(figsize=(10, 10))\n",
-    "scoreboard = Scoreboard()\n",
-    "plot_spacing = 1000\n",
-    "plotted_steps = 0\n",
-    "\n",
-    "R = np.zeros((plot_spacing, 1))\n",
-    "r_trace = np.zeros(n_steps // plot_spacing)\n",
-    "\n",
-    "for step in range(n_steps):\n",
-    "    new_X, new_q = pick_greedy_action(q, p1, epsilon)\n",
-    "    outcomes = game_engine.player_advance([new_X[1]])\n",
-    "    scoreboard.track_outcome(outcomes[p1])\n",
-    "\n",
-    "    update_q(q, old_X, new_X, new_q, outcomes[p1], n_epochs, lr=learning_rate)\n",
-    "\n",
-    "    epsilon *= epsilon_decay\n",
-    "    epsilon_trace[step] = epsilon\n",
-    "    R[step % plot_spacing, 0] = reinforcement(outcomes[p1])\n",
-    "    old_X = new_X\n",
-    "    old_q = new_q\n",
-    "\n",
-    "    if step >= plotted_steps:\n",
-    "        r_trace[plotted_steps // plot_spacing] = np.mean(R)\n",
-    "        plotted_steps += plot_spacing\n",
-    "        scoreboard.flush()\n",
-    "        fig.clf()\n",
-    "        plot_status(q, step, epsilon_trace, r_trace)\n",
-    "        scoreboard.all_goals = 0\n",
-    "        clear_output(wait=True)\n",
-    "        display(fig)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "269ac824-1568-49aa-a020-9a57ee59ae49",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Draw is now True.\n"
-     ]
-    }
-   ],
-   "source": [
-    "game_engine.toggle_draw()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "36a2d897-15a8-47a4-953b-a159af0ad881",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "epsilon = 0\n",
-    "for step in range(500):\n",
-    "    new_X, _ = pick_greedy_action(q, p1, epsilon)\n",
-    "    game_engine.player_advance([new_X[1]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "b77b2db1-e928-4cd8-ae98-7f8ac9b1326f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inferior_table = qtsnake.load_q('inferior_qt.npy')\n",
-    "superior_table = qtsnake.load_q('superior_qt.npy')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "1022bbdf-c68d-4e02-89e0-9d71470d9b8e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "epsilon = 0\n",
-    "n_steps = 1500"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "d67ba96c-9b42-47d2-a88f-a94335bd6967",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Game starting with 3 players.\n"
-     ]
-    }
-   ],
-   "source": [
-    "game_engine = multiplayer.Playfield(window_width=WINDOW_WIDTH,\n",
-    "                                    window_height=WINDOW_HEIGHT,\n",
-    "                                    units=10,\n",
-    "                                    g_speed=100,\n",
-    "                                    s_size=1)\n",
-    "t1 = game_engine.add_player()\n",
-    "t2 = game_engine.add_player()\n",
-    "n1 = game_engine.add_player()\n",
-    "game_engine.start_game()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "c5be5beb-e92c-42ad-9076-c28394560122",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "q_table = qtsnake.QSnake(game_engine)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "314d0836-5c99-4de3-91c8-e563fed61e6c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for step in range(n_steps):\n",
-    "    # table 1\n",
-    "    _, t1_action = q_table.pick_greedy_action(inferior_table, t1, epsilon)\n",
-    "\n",
-    "    # table 2\n",
-    "    _, t2_action = q_table.pick_greedy_action(superior_table, t2, epsilon)\n",
-    "\n",
-    "    # network 1\n",
-    "    n1_state_action, _ = pick_greedy_action(q, n1, epsilon)\n",
-    "    game_engine.player_advance([t1_action,\n",
-    "                                t2_action,\n",
-    "                                n1_state_action[1]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2c75448e-3216-48f1-b649-938711cd4870",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/revised_snake_q_table.ipynb b/revised_snake_q_table.ipynb
deleted file mode 100644
index c9934bc..0000000
--- a/revised_snake_q_table.ipynb
+++ /dev/null
@@ -1,745 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "85da5df2-c926-417c-bd7b-d214ad31ebe1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pygame 2.5.1 (SDL 2.28.2, Python 3.11.5)\n",
-      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from collections import namedtuple\n",
-    "from IPython.core.debugger import Pdb\n",
-    "\n",
-    "from GameEngine import multiplayer\n",
-    "Point = namedtuple('Point', 'x, y')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d264ae4a-380c-47b6-9b29-c6fe16c6399c",
-   "metadata": {},
-   "source": [
-    "### New Game Implementation\n",
-    "\n",
-    "I have an improved game implementation which allows for multiplayer snake games, as well as simplified training. This notebook will go over both of these, including an implementation of q-table learning, as well as a match between a manually filled out q-table and a learned one.\n",
-    "\n",
-    "Let's start by initializing the engine object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2a382240-906d-474f-94c0-9af1a5de97ae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# defines game window size and block size, in pixels\n",
-    "WINDOW_WIDTH = 640\n",
-    "WINDOW_HEIGHT = 480\n",
-    "GAME_UNITS = 80"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "976eff80-c50a-492e-a49b-975d9905e274",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "game_engine = multiplayer.Playfield(window_width=WINDOW_WIDTH,\n",
-    "                                    window_height=WINDOW_HEIGHT,\n",
-    "                                    units=GAME_UNITS,\n",
-    "                                    g_speed=35,\n",
-    "                                    s_size=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "23de2fca-d31a-497f-9a0c-845c568e5df7",
-   "metadata": {},
-   "source": [
-    "Here is a run-down of the current functions available to programs utilizing the game engine:\n",
-    "\n",
-    "**add_player**: Returns the player's number to the callee (between 0-3, for a total of 4 players). This number can be used with other functions to index that player's state.\n",
-    "\n",
-    "**get_heads_tails_and_goal**: Returns an array of player heads (in order of player number) the locations of all snake tails, as well as the goal location. Each is stored in an array of named tuples.\n",
-    "\n",
-    "**get_viable_actions**: Given a player's id, returns a list of integers corresponding to actions which will not immediately result in the snake's death.\n",
-    "0 = UP\n",
-    "1 = RIGHT\n",
-    "2 = DOWN\n",
-    "3 = LEFT\n",
-    "\n",
-    "**start_game**: Initializes goal, player, score, and playfield objects. Disables the ability to add new players. Enables use of player_advance function.\n",
-    "\n",
-    "**stop_game**: Sets the game_state to false, allowing new players to be added.\n",
-    "\n",
-    "**cleanup**: Quits the pygame window.\n",
-    "\n",
-    "**player_advance**: Given an array corresponding to each player's action (integers), returns a list of collision results, and updates the internal game state.\n",
-    "\n",
-    "**toggle_draw**: Turns off/on the game UI for faster training.\n",
-    "\n",
-    "Let's do a test of these functions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "64470c1a-ddc6-4ce1-b6d4-f1e17e3d0176",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Game starting with 1 players.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p1 = game_engine.add_player()\n",
-    "game_engine.start_game()\n",
-    "p1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "28d7fa43-4419-4940-9d4f-d6bd5ccc11ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "viable_actions = game_engine.get_viable_actions(p1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ba45b03a-9c42-48e9-a259-5d01e667157d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<CollisionType.GOAL: 1>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "game_engine.player_advance([np.random.choice(viable_actions)])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "df7170ae-e95d-404b-b632-e8e9de46d69e",
-   "metadata": {},
-   "source": [
-    "If you looked at the UI for this last statement, you should have seen that the game moved the snake (yellow) in a random direction away from immediate death."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "144bc5ff-756c-4e07-a855-4020a4474d52",
-   "metadata": {},
-   "source": [
-    "### State-sensing methods, creating and reading a q-table\n",
-    "Now, we can start redesigning some functions used to allow the snake to play intelligently. We'll use a multi-dimensional numpy array to store the rewards corresponding to each state and action. This is called a q-function, or a q-table in this case.\n",
-    "\n",
-    "How many states do I need? Seeing how the new **get_viable_actions** method already prevents the snake from choosing life-ending moves, the snake is no longer tasked with learning or memorizing it.\n",
-    "\n",
-    "The snake doesneed to be able to interpret progress towards the goal, so I will reinclude one state-sensing to sense the goal direction. I need only 8 states (with entries for each four actions) to represent our game now."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "88a69876-fbc7-4dc1-a471-d8449fada4e4",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0.]])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "goal_relations = 8\n",
-    "actions = 4\n",
-    "q = np.zeros((goal_relations,\n",
-    "              actions))\n",
-    "q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "3e15744e-1251-4315-8a4c-ed5788d04478",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sense_goal(head, goal):\n",
-    "    '''\n",
-    "    maps head and goal location onto an\n",
-    "    integer corresponding to approx location\n",
-    "    '''\n",
-    "    diffs = Point(goal.x - head.x, goal.y - head.y)\n",
-    "\n",
-    "    if diffs.x == 0 and diffs.y <  0:\n",
-    "        return 0\n",
-    "    if diffs.x >  0 and diffs.y <  0:\n",
-    "        return 1\n",
-    "    if diffs.x >  0 and diffs.y == 0:\n",
-    "        return 2\n",
-    "    if diffs.x >  0 and diffs.y >  0:\n",
-    "        return 3\n",
-    "    if diffs.x == 0 and diffs.y >  0:\n",
-    "        return 4\n",
-    "    if diffs.x <  0 and diffs.y >  0:\n",
-    "        return 5\n",
-    "    if diffs.x <  0 and diffs.y == 0:\n",
-    "        return 6\n",
-    "    return 7"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "addf716b-892c-4f7f-b71f-c6af8779dff7",
-   "metadata": {},
-   "source": [
-    "I will use the getter provided by my engine, which queries various statistics about all agents in the game:\n",
-    "1. An array of head positions\n",
-    "2. An array of all tail locations\n",
-    "3. The goal location"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "7b5f0d57-8e26-4f95-b7ea-a6810936ad5d",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([Point(x=480, y=0)],\n",
-       " [Point(x=480, y=0), Point(x=560, y=0)],\n",
-       " Point(x=0, y=400))"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "game_engine.get_heads_tails_and_goal()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3fd47ce-55fe-4d2f-9147-8848193f7ca1",
-   "metadata": {},
-   "source": [
-    "Now to use sense_goal as an index into our q table:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "85e2bab1-c98b-400e-be41-a47ccd4bc163",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def index_actions(q, id):\n",
-    "    '''\n",
-    "    given q, player_id, an array of heads,\n",
-    "    and the goal position,\n",
-    "    indexes into the corresponding expected\n",
-    "    reward of each action\n",
-    "    '''\n",
-    "    heads, tails, goal = game_engine.get_heads_tails_and_goal()\n",
-    "    state = sense_goal(heads[id], goal)\n",
-    "    return state, q[state, :]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33ae53a8-989a-410c-a04f-2ac76561ce21",
-   "metadata": {},
-   "source": [
-    "Returning state here simplifies some logic later when I train the agent. It will be passed along to my next function, but it can be ignored for now."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "3808c200-2f67-43c4-a80f-c4c17dcfeacf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0., 0., 0., 0.])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_, rewards = index_actions(q, p1)\n",
-    "rewards"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a2ef7f7-f6f7-4610-8e98-1d389327f3e8",
-   "metadata": {},
-   "source": [
-    "In our learning agent, these actions will obviously be associated with different expected rewards. But it is not enough to take the best reward, because the positions of the hazards have not been accounted for. I chose to implement a replacement argmin/max function to select actions from this table, which generates new actions in order from highest expected reward to lowest expected reward."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "a172e347-75b7-4b0a-8dcc-07a6ba04f77e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def argmin_gen(rewards):\n",
-    "    rewards = rewards.copy()\n",
-    "    for i in range(rewards.size):\n",
-    "        best_action = np.argmin(rewards)\n",
-    "        rewards[best_action] = float(\"inf\")\n",
-    "        yield best_action"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "9674f225-e7df-4551-baa8-29eb23fcc1d5",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "for action in argmin_gen(rewards):\n",
-    "    print(action)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b9bed101-661c-4e61-b8ad-94bbc1900e03",
-   "metadata": {},
-   "source": [
-    "How will we use this? If the action generated is not a viable action, we will take the next best action.\n",
-    "\n",
-    "What if no actions are viable? Then the agent has boxed itself in, and it doesn't matter what action we choose.\n",
-    "\n",
-    "Previously, I reset the snake if it got stuck in a learning-loop. I will instead use epsilon, as it gives me a bit more control over training. Here is my greedy-action selector function, combining the work of all the previous code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "d3d65397-62a9-47b6-9c84-808282656f80",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def pick_greedy_action(q, id, epsilon):\n",
-    "    viable_actions = game_engine.get_viable_actions(id)\n",
-    "    state, rewards = index_actions(q, id)\n",
-    "\n",
-    "    if np.random.uniform() < epsilon:\n",
-    "        return (state, np.random.choice(viable_actions)) if viable_actions.size > 0 else (state, 0)\n",
-    "    for action in argmin_gen(rewards):\n",
-    "        if action in viable_actions:\n",
-    "            return (state, action)\n",
-    "    return (state, 0) # death"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2169ac2b-df83-4f53-8a83-b35a2d1a521a",
-   "metadata": {},
-   "source": [
-    "We'll set up epsilon to decay over our 500-step test..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "a5932d47-adbe-46de-b91e-582e40faf369",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_steps = 200\n",
-    "epsilon = 1\n",
-    "final_epsilon = 0.001\n",
-    "epsilon_decay =  np.exp(np.log(final_epsilon) / (n_steps))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee7b066a-330d-4cdd-bbb2-9d2a7ad2ceb4",
-   "metadata": {},
-   "source": [
-    "And watch the snake explore:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "6beff583-e32a-4c15-8fcb-f5b5d45ad548",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for step in range(n_steps):\n",
-    "    _, p1_action = pick_greedy_action(q, p1, epsilon)\n",
-    "    game_engine.player_advance([p1_action])\n",
-    "    epsilon *= epsilon_decay"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ee2c2e6-933d-460e-b2fc-f5bf1f22e381",
-   "metadata": {},
-   "source": [
-    "This snake obviously has no prior knowledge of how to earn the most reward, but it still does remarkably well because it is not allowed to die. It behaves as expected, favoring up and right when it is not forced to choose a random action.\n",
-    "\n",
-    "Our q_table only has 32 values as a result of removing the 16 danger states... It would be incredibly easy to manually select reward values to fill our q_table with..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "7af51359-872c-4d1b-b178-e27cf86eb3cc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "set_q = np.array([[-10., -2., 0., -2.],\n",
-    "                 [-5., -5., 0., 0.],\n",
-    "                 [-2., -10., 2., 0.],\n",
-    "                 [0., -5., -5., 0.],\n",
-    "                 [0., -2., -10., -2.],\n",
-    "                 [0., 0., -5., -5.],\n",
-    "                 [-2., 0., -2., -10.],\n",
-    "                 [-5., 0., 0., -5.]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "4da4d318-f7e0-412b-8545-8fd346e167b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "epsilon = 0\n",
-    "for step in range(n_steps):\n",
-    "    _, p1_action = pick_greedy_action(set_q, p1, epsilon)\n",
-    "    game_engine.player_advance([p1_action])\n",
-    "    epsilon *= epsilon_decay"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c36ab97-2ca0-4468-8d4c-ebd1e4deec23",
-   "metadata": {},
-   "source": [
-    "And the snake already plays optimally, no learning required.\n",
-    "\n",
-    "Now that we have these methods, I will create functions to allow the snake to learn by its own, and then pair it off against the q-table I just built."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b9968af-0ec2-4b92-a19d-50912703dd4a",
-   "metadata": {},
-   "source": [
-    "### Learning and Temporal Difference"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce537e44-ac8c-4f09-b89d-a330f13277da",
-   "metadata": {},
-   "source": [
-    "I will be using the temporal difference equation as the key learning element of my reinforcement function. In theory, it allows me adjust the expected reward of a state to agree with its observed successor. In practice, it will allow out agent to take actions that previously led it closer to the goal.\n",
-    "\n",
-    "In order to use this equation, I simply need to create a function that takes the current state/action/outcome, and the previous state/action, as this will be updated in cases where the agent did not reach the goal.\n",
-    "\n",
-    "When the agent does reach the goal, I will manually set that state and action to the best reward, 0. Remember that the q-table is initialized with zeros, meaning untravelled actions are pre-assigned good rewards. Both this and epsilon will encourage exploration.\n",
-    "\n",
-    "Here is the complete function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "a0d3942a-af6a-41f3-be74-167e3abaae0b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_q(q, old_state_action, new_state_action, outcome, lr=0.05):\n",
-    "    if outcome == multiplayer.CollisionType.GOAL:\n",
-    "        q[new_state_action[0], new_state_action[1]] = 0\n",
-    "    else:\n",
-    "        td_error = -1 + q[new_state_action[0], new_state_action[1]] - q[old_state_action[0], old_state_action[1]]\n",
-    "        q[old_state_action[0], old_state_action[1]] += lr * td_error"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "01b21e01-174e-4fdd-ad70-dcc1e6483fb2",
-   "metadata": {},
-   "source": [
-    "Now all that is needed is the training loop. I have high expectations for this agent, so I will only allow it 1500 moves to train itself! Here is where the outputs of pick_greedy_action come in handy:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "95a2ec72-e30c-4730-a876-21f054d3727f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_steps = 1500\n",
-    "epsilon = 1\n",
-    "final_epsilon = 0.001\n",
-    "epsilon_decay =  np.exp(np.log(final_epsilon) / (n_steps))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "a71dc022-5e51-46f8-bfed-37b95243fc5e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p1_old_s_a = pick_greedy_action(q, p1, epsilon) # state, action\n",
-    "game_engine.player_advance([p1_old_s_a[1]])\n",
-    "\n",
-    "for step in range(n_steps):\n",
-    "    p1_new_s_a = pick_greedy_action(q, p1, epsilon) # state, action\n",
-    "    outcome = game_engine.player_advance([p1_new_s_a[1]])\n",
-    "\n",
-    "    update_q(q, p1_old_s_a, p1_new_s_a, outcome)\n",
-    "\n",
-    "    epsilon *= epsilon_decay\n",
-    "    p1_old_s_a = p1_new_s_a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6cbf429-c790-4bb9-8775-ed067844ab4e",
-   "metadata": {},
-   "source": [
-    "The results look promising. Here is everything it learned:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "ad12d31a-a1ec-45af-89b0-f66ca375b524",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-1.85942515, -0.28461569, -0.1475    , -2.25187465],\n",
-       "       [-6.22032336, -2.97794369, -0.54294824, -0.73044876],\n",
-       "       [-1.36441677, -5.89002527, -0.37131058, -0.44722626],\n",
-       "       [-4.05086759, -5.41891136, -2.6667349 , -1.52778296],\n",
-       "       [-0.74803435, -1.03051018, -5.33579039, -1.17403081],\n",
-       "       [-1.14848949, -1.11816252, -5.95403265, -2.19930379],\n",
-       "       [-0.91983985, -0.43064397, -0.97973069, -6.54929252],\n",
-       "       [-5.1999968 , -0.75777597, -0.38182729, -2.87468477]])"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2414728-c36c-45d6-8f2d-18e78e482054",
-   "metadata": {},
-   "source": [
-    "### Multiplayer Demonstration, Saving Tables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "24c73f20-2853-4ba7-a24e-22c5a4b8da5e",
-   "metadata": {},
-   "source": [
-    "The most entertaining way to test the success of my implementation is pair the agents q and set_q against each other. I will first stop and set up a new game:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "a93f0fe5-2bc4-45d7-ba31-2306efaa9806",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Game over!\n",
-      "Game starting with 2 players.\n"
-     ]
-    }
-   ],
-   "source": [
-    "game_engine.stop_game()\n",
-    "p2 = game_engine.add_player()\n",
-    "game_engine.start_game()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47a1adde-d95d-444e-9688-1290764f8cfa",
-   "metadata": {},
-   "source": [
-    "Now, I can simply call player advance with both player's actions in order, and the engine will handle the rest. I will define a new game loop, similar to the previous one:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "d5b5d089-cb66-47de-b352-36c13fd7dead",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "epsilon = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "4de326e6-82dc-48df-8920-d28d0154fbd9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for step in range(n_steps):\n",
-    "    # p1\n",
-    "    _, p1_action = pick_greedy_action(set_q, p1, epsilon)\n",
-    "\n",
-    "    # p2\n",
-    "    p2_new_s_a = pick_greedy_action(q, p2, epsilon) # state, action\n",
-    "    \n",
-    "    game_engine.player_advance([p1_action, p2_new_s_a[1]])\n",
-    "\n",
-    "    epsilon *= epsilon_decay\n",
-    "    p2_old_s_a = p2_new_s_a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "820d7e5f-c3e9-4dae-82ce-3c9188d8a8d8",
-   "metadata": {},
-   "source": [
-    "The learned agent normally plays significantly worse than the artificially-learned one, which is okay given I hardly spent time optimizing the number of steps and learning rate. I plan to compare both of the agents again my neural-network approach, so the last this I will do is save the q_tables to a file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "95227c89-e7db-4923-9160-dacfa1cf4af8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.save('superior_qt.npy', set_q)\n",
-    "np.save('inferior_qt.npy', q)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9eec33d8-9a65-426a-8ad5-8ffb2cbe2541",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/two_revised_snake_q_network.ipynb b/two_revised_snake_q_network.ipynb
new file mode 100644
index 0000000..9b9028d
--- /dev/null
+++ b/two_revised_snake_q_network.ipynb
@@ -0,0 +1,603 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "73c6d255-0c32-4895-9a22-e95eadb25103",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pygame 2.5.1 (SDL 2.28.2, Python 3.11.5)\n",
+      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from collections import namedtuple\n",
+    "from IPython.core.debugger import Pdb\n",
+    "from IPython.display import display, clear_output\n",
+    "\n",
+    "from QNetwork import neuralnetwork_regression as nn\n",
+    "from GameEngine import multiplayer\n",
+    "from QTable import qtsnake\n",
+    "\n",
+    "Point = namedtuple('Point', 'x, y')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3aab739-e016-4700-89c9-41f3c2f536cf",
+   "metadata": {},
+   "source": [
+    "### Representing Q-function using Neural Networks\n",
+    "\n",
+    "In the last notebook, I represented my Q-function in a simple lookup table. This notebook offers a difference approach by using a neural network. The function we want the neural network to learn is, of course, the snake's Q-function, which maps a state-action pair onto an expected reward.\n",
+    "\n",
+    "#### Benefits of a Q-network\n",
+    "\n",
+    "The distinction between a Q-table and Q-network is that a Q-network contains and updates a set of parameters (weights) which summarize previously seen data. A Q-table cannot do this, and thus is completely clueless in situations where it recieves an input it has either not seen, or has not been trained on. In theory, this allows a neural network to not only represent environments with many more states, but also the ability to make guesses about in 'gaps' in its learning.\n",
+    "\n",
+    "This notebook will go over training of a simple q-network, which maps a total of 32 different combinations of states and actions onto rewards, much like the previous q-table implementation from ***one_revised_snake_q_table.ipynb***.\n",
+    "\n",
+    "First, I will set up the game environment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "682a7036-4f0d-4f3d-b147-6355c0a2f93e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# defines game window size and block size, in pixels\n",
+    "WINDOW_WIDTH = 640\n",
+    "WINDOW_HEIGHT = 480\n",
+    "GAME_UNITS = 80"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "41cfbec9-e14e-4c58-95dd-2e3fb1788e72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "game_engine = multiplayer.Playfield(window_width=WINDOW_WIDTH,\n",
+    "                                    window_height=WINDOW_HEIGHT,\n",
+    "                                    units=GAME_UNITS,\n",
+    "                                    g_speed=35,\n",
+    "                                    s_size=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "804a13dc-7dd4-43f0-bc47-e781bc022075",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Game starting with 1 players.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p1 = game_engine.add_player()\n",
+    "game_engine.start_game()\n",
+    "p1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34efdb66-7a8e-4b48-a015-d1eb8a029915",
+   "metadata": {},
+   "source": [
+    "Training thousands of steps is a little bit slow with the graphics on. It makes only a small difference here, but it provides little information anyways:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b94f16d4-65bb-4150-bdc0-6cc648e3cb7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Draw is now False.\n"
+     ]
+    }
+   ],
+   "source": [
+    "game_engine.toggle_draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43cefedf-e005-4910-9b4c-953697aa3f26",
+   "metadata": {},
+   "source": [
+    "### State-sensing methods, defining reinforcement and greedy-action selector\n",
+    "\n",
+    "I have also imported the aforementioned q_table implementation as qtsnake. It will come back in the end of the notebook when I pair the q_table and q_network against each other, but to make the game fair, I'll use the exact same state-sensing method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "71c97804-74d3-4248-bdb7-5519aa02b556",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function QTable.qtsnake.sense_goal(head, goal)>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qtsnake.sense_goal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e065f223-9e19-4f21-ba75-8d44fc62d353",
+   "metadata": {},
+   "source": [
+    "Even though I plan to only call it when selecting a greedy_action, I'll wrap it in a neat 'query_state' function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "26b8f8bf-ad08-40f8-847f-88351e262c1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def query_state(id):\n",
+    "    '''\n",
+    "    given a player's id,\n",
+    "    returns their state\n",
+    "    '''\n",
+    "    heads, _, goal = game_engine.get_heads_tails_and_goal()\n",
+    "    return np.array(qtsnake.sense_goal(heads[id], goal))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d61e508-0661-4893-a720-f0a511c52809",
+   "metadata": {},
+   "source": [
+    "And a reinforcement function. Because I took the requirement to sense danger away, we only need two outputs from the reinforcement function.\n",
+    "\n",
+    "The output of this function was chosen due to being the best-performing. It is possible the reward for GOAL should be higher or lower. In actuality, the reinforcement for non-goals will never be used. I prefer the simplicity of using the discount factor to force agents to the goal quickly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0af0a115-83b9-498a-8228-dc79580131f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reinforcement(outcome):\n",
+    "    '''\n",
+    "    given an outcome of an action,\n",
+    "    returns associated reward\n",
+    "    '''\n",
+    "    if outcome == multiplayer.CollisionType.GOAL:\n",
+    "        return -3\n",
+    "    return 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45e6040c-9aae-4f9e-8ef6-cf23b4043622",
+   "metadata": {},
+   "source": [
+    "Here is the first real interesting function. It takes its implementation largely from the marble example, but it accepts and returns parameters as closely to the previous q-table version.\n",
+    "\n",
+    "In essence, I ask the game the viable actions for a player, take into account our current state, and choose the action with the greatest expected reward, or a random action. This is called epsilon greedy selection.\n",
+    "\n",
+    "When calling use on the network, it maps a state and action onto a reward, just the same as indexing the q-table. We return the expected reward for this action in addition, because it is needed later for learning with discounted rewards."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a76fd63a-478a-43ad-91ce-df1dff03e565",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pick_greedy_action(q_net, id, epsilon):\n",
+    "    '''\n",
+    "    given a q network, the id of the player\n",
+    "    taking action, and a randomization factor,\n",
+    "    returns the most rewarding non-lethal action\n",
+    "    or a non-lethal random action and expected reward\n",
+    "    '''\n",
+    "    viable_actions = game_engine.get_viable_actions(id)\n",
+    "    state = query_state(id)\n",
+    "\n",
+    "    if viable_actions.size < 1:\n",
+    "        best_action = 0\n",
+    "    elif np.random.uniform() < epsilon:\n",
+    "        best_action = np.random.choice(viable_actions)\n",
+    "    else:\n",
+    "        qs = [q_net.use(np.hstack(\n",
+    "            (state, action)).reshape((1, -1))) for action in viable_actions]\n",
+    "        best_action = viable_actions[np.argmin(qs)]\n",
+    "\n",
+    "    X = np.hstack((state, best_action))\n",
+    "    q = q_net.use(X.reshape((1, -1)))\n",
+    "\n",
+    "    return X, q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "06cd085e-77f4-4a22-9b1f-ec364b7737c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_q(q, old_X, new_X, new_q, outcome, n_epochs, discount=0.9, lr=0.2):\n",
+    "    '''\n",
+    "    given a q network, the previous state/action pair,\n",
+    "    the new state/action pair, the expected next reward,\n",
+    "    the outcome of the last action, the number of epochs,\n",
+    "    a discount factor (gamma), and the learning rate\n",
+    "    updates q with discounted rewards.\n",
+    "    '''\n",
+    "    reward = reinforcement(outcome)\n",
+    "    if outcome == multiplayer.CollisionType.GOAL:\n",
+    "        q.train(np.array([new_X]),\n",
+    "                np.array([reward]) + np.array([[reward]]),\n",
+    "                n_epochs, lr, method='sgd', verbose=False)\n",
+    "    else:\n",
+    "        q.train(np.array([old_X]),\n",
+    "                discount * np.array([new_q]), n_epochs,\n",
+    "                lr, method='sgd', verbose=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f51c3238-c918-40a5-bf38-1456f4ed4ff5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma = 0.9\n",
+    "n_epochs = 10\n",
+    "learning_rate = 0.015\n",
+    "\n",
+    "hidden_layers = [15]\n",
+    "q = nn.NeuralNetwork(2, hidden_layers, 1)\n",
+    "q.setup_standardization([5, 3.5], [4, np.sqrt(5.25)], [-.1], [0.2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "072ef9b7-86ec-4cbf-a315-dd6b4019fce6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_steps = 10000\n",
+    "epsilon = 1\n",
+    "final_epsilon = 0.05\n",
+    "epsilon_decay =  np.exp(np.log(final_epsilon) / (n_steps))\n",
+    "epsilon_trace = np.zeros(n_steps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "720a04aa-b53f-42d7-adf8-7c1a0958ff04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Scoreboard():\n",
+    "    ''' tracks game statistics '''\n",
+    "    def __init__(self):\n",
+    "        self.all_goals = 0\n",
+    "        self._deaths = 0\n",
+    "        self._goals = 0\n",
+    "        self._max_goals = 0\n",
+    "\n",
+    "        self.goals = []\n",
+    "        self.deaths = []\n",
+    "        self.max_goals = []\n",
+    "\n",
+    "    def track_outcome(self, outcome):\n",
+    "        if outcome == multiplayer.CollisionType.GOAL:\n",
+    "            self._goals += 1\n",
+    "            self.all_goals += 1\n",
+    "            if self._goals > self._max_goals:\n",
+    "                self._max_goals = self._goals\n",
+    "        elif outcome == multiplayer.CollisionType.DEATH:\n",
+    "            self._deaths += 1\n",
+    "            self._goals = 0\n",
+    "\n",
+    "    def flush(self):\n",
+    "        self.goals.append(self._goals)\n",
+    "        self.deaths.append(self._deaths)\n",
+    "        self.max_goals.append(self._max_goals)\n",
+    "\n",
+    "        self._reset()\n",
+    "\n",
+    "    def _reset(self):\n",
+    "        self._deaths = 0\n",
+    "        self._goals = 0\n",
+    "        self._max_goals = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c86cea77-c3b9-44fa-becd-2d04d49b92cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_status(q, step, epsilon_trace, r_trace):\n",
+    "    \n",
+    "    plt.subplot(4, 3, 1)\n",
+    "    plt.plot(epsilon_trace[:step + 1])\n",
+    "    plt.ylabel('Random Action Probability ($\\epsilon$)')\n",
+    "    plt.ylim(0, 1)\n",
+    "\n",
+    "    plt.subplot(4, 3, 2)\n",
+    "    plt.plot(scoreboard.deaths)\n",
+    "    plt.ylabel('Deaths')\n",
+    "\n",
+    "    plt.subplot(4, 3, 3)\n",
+    "    plt.plot(scoreboard.goals)\n",
+    "    plt.ylabel('Goals')\n",
+    "\n",
+    "    plt.subplot(4, 3, 4)\n",
+    "    plt.plot(scoreboard.max_goals)\n",
+    "    plt.ylabel('Max Score')\n",
+    "\n",
+    "    '''\n",
+    "    plt.subplot(4, 3, 5)\n",
+    "    plt.plot(r_trace[:step + 1], alpha=0.5)\n",
+    "    binSize = 20\n",
+    "    if step+1 > binSize:\n",
+    "        # Calculate mean of every bin of binSize reinforcement values\n",
+    "        smoothed = np.mean(r_trace[:int(step / binSize) * binSize].reshape((int(step / binSize), binSize)), axis=1)\n",
+    "        plt.plot(np.arange(1, 1 + int(step / binSize)) * binSize, smoothed)\n",
+    "    plt.ylabel('Mean reinforcement')\n",
+    "    '''\n",
+    "\n",
+    "    plt.subplot(4, 3, 6)\n",
+    "    q.draw(['$o$', '$a$'], ['q'])\n",
+    "\n",
+    "    plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "00ca3585-8a11-4fd5-93d7-8e73bfc31e81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "old_X, old_q = pick_greedy_action(q, p1, epsilon)\n",
+    "game_engine.player_advance([old_X[1]])\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "scoreboard = Scoreboard()\n",
+    "plot_spacing = 1000\n",
+    "plotted_steps = 0\n",
+    "\n",
+    "R = np.zeros((plot_spacing, 1))\n",
+    "r_trace = np.zeros(n_steps // plot_spacing)\n",
+    "\n",
+    "for step in range(n_steps):\n",
+    "    new_X, new_q = pick_greedy_action(q, p1, epsilon)\n",
+    "    outcomes = game_engine.player_advance([new_X[1]])\n",
+    "    scoreboard.track_outcome(outcomes[p1])\n",
+    "\n",
+    "    update_q(q, old_X, new_X, new_q, outcomes[p1], n_epochs, lr=learning_rate)\n",
+    "\n",
+    "    epsilon *= epsilon_decay\n",
+    "    epsilon_trace[step] = epsilon\n",
+    "    R[step % plot_spacing, 0] = reinforcement(outcomes[p1])\n",
+    "    old_X = new_X\n",
+    "    old_q = new_q\n",
+    "\n",
+    "    if step >= plotted_steps:\n",
+    "        r_trace[plotted_steps // plot_spacing] = np.mean(R)\n",
+    "        plotted_steps += plot_spacing\n",
+    "        scoreboard.flush()\n",
+    "        fig.clf()\n",
+    "        plot_status(q, step, epsilon_trace, r_trace)\n",
+    "        scoreboard.all_goals = 0\n",
+    "        clear_output(wait=True)\n",
+    "        display(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "269ac824-1568-49aa-a020-9a57ee59ae49",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Draw is now True.\n"
+     ]
+    }
+   ],
+   "source": [
+    "game_engine.toggle_draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "36a2d897-15a8-47a4-953b-a159af0ad881",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epsilon = 0\n",
+    "for step in range(500):\n",
+    "    new_X, _ = pick_greedy_action(q, p1, epsilon)\n",
+    "    game_engine.player_advance([new_X[1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b77b2db1-e928-4cd8-ae98-7f8ac9b1326f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inferior_table = qtsnake.load_q('inferior_qt.npy')\n",
+    "superior_table = qtsnake.load_q('superior_qt.npy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1022bbdf-c68d-4e02-89e0-9d71470d9b8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epsilon = 0\n",
+    "n_steps = 1500"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d67ba96c-9b42-47d2-a88f-a94335bd6967",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Game starting with 3 players.\n"
+     ]
+    }
+   ],
+   "source": [
+    "game_engine = multiplayer.Playfield(window_width=WINDOW_WIDTH,\n",
+    "                                    window_height=WINDOW_HEIGHT,\n",
+    "                                    units=10,\n",
+    "                                    g_speed=100,\n",
+    "                                    s_size=1)\n",
+    "t1 = game_engine.add_player()\n",
+    "t2 = game_engine.add_player()\n",
+    "n1 = game_engine.add_player()\n",
+    "game_engine.start_game()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c5be5beb-e92c-42ad-9076-c28394560122",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q_table = qtsnake.QSnake(game_engine)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "314d0836-5c99-4de3-91c8-e563fed61e6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for step in range(n_steps):\n",
+    "    # table 1\n",
+    "    _, t1_action = q_table.pick_greedy_action(inferior_table, t1, epsilon)\n",
+    "\n",
+    "    # table 2\n",
+    "    _, t2_action = q_table.pick_greedy_action(superior_table, t2, epsilon)\n",
+    "\n",
+    "    # network 1\n",
+    "    n1_state_action, _ = pick_greedy_action(q, n1, epsilon)\n",
+    "    game_engine.player_advance([t1_action,\n",
+    "                                t2_action,\n",
+    "                                n1_state_action[1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c75448e-3216-48f1-b649-938711cd4870",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
-- 
cgit v1.2.3