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|
{
"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 = 480\n",
"WINDOW_HEIGHT = 320\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. So, I introduced a function to turn the drawing off."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "acabac69-a92d-4415-b4ef-251fd1e965f7",
"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 now the reinforcement function. Because I took the requirement to sense danger away, we only need two outputs from the reinforcement function. In almost every case, the snake is not allowed to choose an action that would collide with its own tail.\n",
"\n",
"The output of this function was chosen due to being the best-performing. In reality, 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. This is because, with larger discount, the snake prioritizes actions that result in more immediate rewards. An alternative approach which a tried is to punish the agent for each unneccessary step."
]
},
{
"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": [
"For this version of the epsilon greedy function, I wanted an interface similar to the ***one_revised_snake_q_table.ipynb*** notebook. The function operates in the same way, by accumulating the expected reward for each action taken in a state into a list, and then returning the argmin of those actions. I 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": "markdown",
"id": "0c87558b-e6ce-4db2-a0cb-7bdb5dd70c75",
"metadata": {},
"source": [
"### Q-Learning with Temporal Difference, One Sample at a Time\n",
"\n",
"Unlike the marble implementation, I have created a similar training loop to what was observed in the q-table, without the use of a make samples function. This means I adjust each weight for a single sample at a time (batch size 1), assigning the output of each intermediate step to the discounted rewards of future steps, 'bootstrapping' the learning process similar to the temporal difference equation. Remember, 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'')..."
]
},
{
"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": "markdown",
"id": "93e8aa26-d334-49d3-8640-ede35ba6f1ae",
"metadata": {},
"source": [
"#### Training\n",
"\n",
"In this case, I already know our game world is limited to 32 inputs. In this minimal case, I don't neccessarily care if the network is generalizable, so there is no real need for a test set, and no real downside of overfitting. My learning process will simply run the experiment for a set amount of steps. This is not to say that my approach may not be generalizable, but that there is no real way to know.\n",
"\n",
"Through use of my exploration strategy, as well as a randomly initialized set of weights, the data passed into the neural network should thouroughly account for all possible inputs.\n",
"\n",
"To start, I initialize a few hyperparameters, discovered largely through trial-and-error, and create a new Q-network object:"
]
},
{
"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 = [10]\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": "markdown",
"id": "ff9cf658-ec13-4810-9443-757b71663bbb",
"metadata": {},
"source": [
"Reminder that gamma is the discount factor, and learning rate controls how quickly the weights are adjusted, much like I used it for the temporal difference equation.\n",
"\n",
"In general, the number of epochs corresponds to the amount of weight updates occur per batch of samples. Often, large numbers result in poor generalizability, which, as mentioned, is not a priority due to the size of the Q-input pool.\n",
"\n",
"Similarly to before, I'll set up epsilon to decay exponentially over a 10,000 step training loop..."
]
},
{
"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": "markdown",
"id": "e2f54fd3-4899-4d83-bd6b-2b50a66a6b26",
"metadata": {},
"source": [
"And create a few classes and methods to plot the results:"
]
},
{
"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=None):\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": "markdown",
"id": "79ad6521-1907-4fb3-8a33-a0e470e0a361",
"metadata": {},
"source": [
"The logic behind this the training loop is the same as the q-table implementation with added calls to the scoreboard and plotting functions, because I took the time to make each function interface the same. If exported this code to a file, I may utilize higher-order functions to allow easy selection of either Q-function:"
]
},
{
"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)\n",
" scoreboard.all_goals = 0\n",
" clear_output(wait=True)\n",
" display(fig)"
]
},
{
"cell_type": "markdown",
"id": "4caa7801-017f-429c-ba14-7331fab1a68b",
"metadata": {},
"source": [
"Like the q-table, the training process occasionally produces a bad agent. This is always due to the agent's exploration finding relatively few goals. To solve this, I specifically select a small envirnoment to train on, as random actions happen to score goals more likely. A slower decay of epsilon would likely improve this further, though this is generally not an issue."
]
},
{
"cell_type": "markdown",
"id": "3bc3c79e-2162-4f3a-9e73-a1d789cc2bb0",
"metadata": {},
"source": [
"#### Viewing the Results: Multiplayer Snake\n",
"\n",
"Now, I'll test the performance of the Q-network first by itself, in a simple get action advance loop. I'll turn on the draw feature:"
]
},
{
"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": "markdown",
"id": "b208136a-90c0-40e9-ac73-e2536e903ae3",
"metadata": {},
"source": [
"If the agent gets stuck, you may want to retrain the agent by running the last few cells.\n",
"\n",
"Now for the fun part: Facing the agents off against each other..."
]
},
{
"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": "markdown",
"id": "4897a058-a527-4d3c-bd08-7f8eac8a6e58",
"metadata": {},
"source": [
"I also make the game really large, to allow the snakes their own space:"
]
},
{
"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": "markdown",
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"And initialize a new q-table object with the table. This object is quite nice, because it is not tied to any one q-table; it simply reads and writes a given q-table:"
]
},
{
"cell_type": "code",
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"q_table = qtsnake.QSnake(game_engine)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "314d0836-5c99-4de3-91c8-e563fed61e6c",
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"source": [
"for step in range(n_steps):\n",
" # table 1 (YELLOW)\n",
" _, t1_action = q_table.pick_greedy_action(inferior_table, t1, epsilon)\n",
"\n",
" # table 2 (RED)\n",
" _, t2_action = q_table.pick_greedy_action(superior_table, t2, epsilon)\n",
"\n",
" # network 1 (PURPLE)\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]])"
]
},
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"YELLOW: Learned Q-Table\n",
"\n",
"RED: Set Q-Table\n",
"\n",
"PURPLE: Learned Q-Network\n",
"\n",
"Often in my games, the q-network finishes second behind the superior, manually set q-table. Sometimes, it is the other way around, dependent on how successful each agent trained, and the luck of goal spawns during the trial. I have also seen it finish first on many occasions. All in all, it seems both the Q-network and Q-table are relatively equally matched in this game representation."
]
},
{
"cell_type": "markdown",
"id": "5d369045-bc0a-4567-99bb-bb04e75f294c",
"metadata": {},
"source": [
""
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