{ "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. 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": [ "" ] }, "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 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. 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.\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 = [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": "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 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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", 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", "text/plain": [ "
" ] }, "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, " ] }, { "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 (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]])" ] }, { "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 }