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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 190, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "\n", | |
| "class SlotMachine(object):\n", | |
| " def __init__(self, num_levers):\n", | |
| " self.num_levers = num_levers\n", | |
| " self.success_rates = [np.random.beta(1,1) for _ in range(num_levers)]\n", | |
| " \n", | |
| " def pull(self, state, i):\n", | |
| " reward = np.random.binomial(1, self.success_rates[i])\n", | |
| " output = np.zeros(self.num_levers)\n", | |
| " output[i] = reward\n", | |
| " gain = reward\n", | |
| " return output * state, gain\n", | |
| " \n", | |
| " def generate_state(self):\n", | |
| " num_in_state = np.random.choice(range(self.num_levers))\n", | |
| " num_in_state = max(1, num_in_state)\n", | |
| " state = np.random.choice(range(self.num_levers), size=num_in_state, replace=False)\n", | |
| " state_vector = np.zeros(self.num_levers)\n", | |
| " for i in state:\n", | |
| " state_vector[i] = 1.\n", | |
| " return state_vector" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 195, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from keras.layers import Input, Dense, Dropout\n", | |
| "from keras.models import Model\n", | |
| "from keras.optimizers import RMSprop\n", | |
| "from collections import deque\n", | |
| "\n", | |
| "class DeepQNetwork(object):\n", | |
| " def __init__(self, slot_machine, memory_length=100):\n", | |
| " self.memory = deque([])\n", | |
| " self.memory_length = memory_length\n", | |
| " self.slot_machine = slot_machine\n", | |
| " self.model = self.build_model()\n", | |
| " \n", | |
| " def act(self):\n", | |
| " state = self.slot_machine.generate_state()\n", | |
| " policy = self.model.predict(np.reshape(state, (1,10)))\n", | |
| " action = np.argmax(self.model.predict(np.reshape(state, (1,10))))\n", | |
| " reward, gain = self.slot_machine.pull(state, action)\n", | |
| " random_gain = self.random_act(state)\n", | |
| " best_gain = self.best_act(state)\n", | |
| " return (state, action, reward, gain, random_gain, best_gain)\n", | |
| " \n", | |
| " def random_act(self, state):\n", | |
| " choices = np.nonzero(state)[0]\n", | |
| " action = np.random.choice(choices)\n", | |
| " _, random_gain = self.slot_machine.pull(state, action)\n", | |
| " return random_gain\n", | |
| " \n", | |
| " def best_act(self, state):\n", | |
| " choices = np.nonzero(state)[0]\n", | |
| " best_reward = max(np.array(self.slot_machine.success_rates)[choices])\n", | |
| " return best_reward\n", | |
| " \n", | |
| " def learn(self, state, action, reward):\n", | |
| " if len(self.memory) > self.memory_length:\n", | |
| " self.memory.popleft()\n", | |
| " recent_memory = self.memory #[-self.memory_length:]\n", | |
| " states, actions, rewards, gains, random_gains, best_gains = zip(*recent_memory)\n", | |
| " best_rate = sum(best_gains)/ float(len(best_gains))\n", | |
| " current_rate= sum(gains)/ float(len(gains))\n", | |
| " random_rate = sum(random_gains)/ float(len(random_gains))\n", | |
| " percent_best = 1. - (best_rate - current_rate) / (best_rate - random_rate)\n", | |
| " print(\"percent best: {} reward rate: {} random rate: {} best rate: {}\".format(percent_best,\n", | |
| " current_rate,\n", | |
| " random_rate,\n", | |
| " best_rate\n", | |
| " ))\n", | |
| " self.model.train_on_batch(np.array(states), np.array(rewards))\n", | |
| " \n", | |
| "\n", | |
| " def build_model(self):\n", | |
| " input_length = self.slot_machine.num_levers\n", | |
| " output_length = self.slot_machine.num_levers\n", | |
| " \n", | |
| " state_input = Input(shape=(input_length,))\n", | |
| " h1 = Dense(10, activation='tanh', kernel_regularizer='l2', bias_regularizer='l2')(state_input)\n", | |
| " d1 = Dropout(0.5)(h1)\n", | |
| " h2 = Dense(10, activation='tanh')(d1)\n", | |
| " #d2 = Dropout(0.5)(h2)\n", | |
| " #h3 = Dense(50, activation='tanh')(d2)\n", | |
| " chosen_action = Dense(output_length, activation='linear')(h2)\n", | |
| " self.model = Model(inputs=[state_input], outputs=[chosen_action])\n", | |
| " self.model.compile(optimizer=RMSprop(lr=0.0001, clipnorm=0.5), loss='mean_squared_error')\n", | |
| " return self.model\n", | |
| " \n", | |
| " def run(self, steps=10000):\n", | |
| " for _ in range(steps):\n", | |
| " state, action, reward, gain, random_gain, best_gain = self.act()\n", | |
| " self.memory.append((state, action, reward, gain, random_gain, best_gain))\n", | |
| " if len(self.memory) > self.memory_length:\n", | |
| " self.learn(state, action, reward)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 196, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "[0.7649665027700249,\n", | |
| " 0.42673473686418517,\n", | |
| " 0.1846129361860512,\n", | |
| " 0.7153497667416178,\n", | |
| " 0.7739713412196318,\n", | |
| " 0.060022694586102314,\n", | |
| " 0.6320066406484348,\n", | |
| " 0.5786114684088347,\n", | |
| " 0.6193197857027226,\n", | |
| " 0.6139720509228324]" | |
| ] | |
| }, | |
| "execution_count": 196, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "slot_machine = SlotMachine(10)\n", | |
| "slot_machine.success_rates" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 197, | |
| "metadata": { | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "percent best: 0.06565533116103184 reward rate: 0.529 random rate: 0.517 best rate: 0.6997726673187863\n", | |
| "percent best: 0.0605316714898011 reward rate: 0.529 random rate: 0.518 best rate: 0.6997230505827579\n", | |
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