303 lines
33 KiB
Plaintext
303 lines
33 KiB
Plaintext
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Deep_Q_Learning_Cart_Pole_balancing.ipynb",
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"provenance": [],
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/PacktPublishing/Hands-On-Computer-Vision-with-PyTorch/blob/master/Chapter16/Deep_Q_Learning_Cart_Pole_balancing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "EAsxgBMbnOlT"
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},
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"source": [
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"import gym\n",
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"import numpy as np\n",
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"import cv2\n",
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"from collections import deque\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"import numpy as np\n",
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"import random\n",
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"from collections import namedtuple, deque\n",
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"import torch\n",
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"import torch.nn.functional as F\n",
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"import torch.optim as optim\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "3ANvz1Z_nWDz"
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},
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"source": [
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"env = gym.make('CartPole-v1')"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "NXyrPGJSnXpp"
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},
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"source": [
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"class DQNetwork(nn.Module):\n",
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" def __init__(self, state_size, action_size):\n",
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" super(DQNetwork, self).__init__()\n",
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" \n",
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" self.fc1 = nn.Linear(state_size, 24)\n",
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" self.fc2 = nn.Linear(24, 24)\n",
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" self.fc3 = nn.Linear(24, action_size)\n",
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" \n",
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" def forward(self, state): \n",
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" x = F.relu(self.fc1(state))\n",
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" x = F.relu(self.fc2(x))\n",
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" x = self.fc3(x)\n",
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" return x\n"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Fxxt2zRGnczv"
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},
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"source": [
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"class Agent():\n",
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" def __init__(self, state_size, action_size):\n",
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" \n",
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" self.state_size = state_size\n",
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" self.action_size = action_size\n",
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" self.seed = random.seed(0)\n",
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"\n",
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" ## hyperparameters\n",
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" self.buffer_size = 2000\n",
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" self.batch_size = 64\n",
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" self.gamma = 0.99\n",
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" self.lr = 0.0025\n",
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" self.update_every = 4 \n",
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"\n",
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" # Q-Network\n",
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" self.local = DQNetwork(state_size, action_size).to(device)\n",
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" self.optimizer = optim.Adam(self.local.parameters(), lr=self.lr)\n",
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"\n",
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" # Replay memory\n",
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" self.memory = deque(maxlen=self.buffer_size) \n",
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" self.experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n",
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" self.t_step = 0\n",
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" def step(self, state, action, reward, next_state, done):\n",
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" # Save experience in replay memory\n",
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" self.memory.append(self.experience(state, action, reward, next_state, done)) \n",
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" # Learn every update_every time steps.\n",
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" self.t_step = (self.t_step + 1) % self.update_every\n",
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" if self.t_step == 0:\n",
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" # If enough samples are available in memory, get random subset and learn\n",
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" if len(self.memory) > self.batch_size:\n",
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" experiences = self.sample_experiences()\n",
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" self.learn(experiences, self.gamma)\n",
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" def act(self, state, eps=0.):\n",
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" # Epsilon-greedy action selection\n",
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" if random.random() > eps:\n",
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" state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n",
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" self.local.eval()\n",
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" with torch.no_grad():\n",
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" action_values = self.local(state)\n",
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" self.local.train()\n",
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" return np.argmax(action_values.cpu().data.numpy())\n",
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" else:\n",
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" return random.choice(np.arange(self.action_size))\n",
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" def learn(self, experiences, gamma): \n",
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" states, actions, rewards, next_states, dones = experiences\n",
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" # Get expected Q values from local model\n",
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" Q_expected = self.local(states).gather(1, actions)\n",
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"\n",
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" # Get max predicted Q values (for next states) from local model\n",
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" Q_targets_next = self.local(next_states).detach().max(1)[0].unsqueeze(1)\n",
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" # Compute Q targets for current states \n",
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" Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))\n",
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" \n",
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" # Compute loss\n",
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" loss = F.mse_loss(Q_expected, Q_targets)\n",
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"\n",
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" # Minimize the loss\n",
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" self.optimizer.zero_grad()\n",
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" loss.backward()\n",
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" self.optimizer.step()\n",
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" def sample_experiences(self):\n",
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" experiences = random.sample(self.memory, k=self.batch_size) \n",
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" states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)\n",
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" actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)\n",
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" rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)\n",
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" next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)\n",
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" dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device) \n",
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" return (states, actions, rewards, next_states, dones)\n",
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"agent = Agent(env.observation_space.shape[0], env.action_space.n)"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Ov7oTYsYnowd"
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},
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"source": [
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"scores = [] # list containing scores from each episode\n",
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"scores_window = deque(maxlen=100) # last 100 scores\n",
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"n_episodes=5000\n",
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"max_t=5000\n",
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"eps_start=1.0\n",
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"eps_end=0.001\n",
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"eps_decay=0.9995\n",
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"eps = eps_start"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "mHOK_FWapNn7",
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"outputId": "9a7484a8-2a53-4647-ec5d-d4249ade4ed1",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 893
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}
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},
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"source": [
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"for i_episode in range(1, n_episodes+1):\n",
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" state = env.reset()\n",
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" state_size = env.observation_space.shape[0]\n",
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" state = np.reshape(state, [1, state_size])\n",
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" score = 0\n",
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" for i in range(max_t):\n",
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" action = agent.act(state, eps)\n",
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" next_state, reward, done, _ = env.step(action)\n",
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" next_state = np.reshape(next_state, [1, state_size])\n",
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" reward = reward if not done or score == 499 else -10\n",
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" agent.step(state, action, reward, next_state, done)\n",
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" state = next_state\n",
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" score += reward\n",
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" if done:\n",
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" break \n",
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" scores_window.append(score) # save most recent score \n",
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" scores.append(score) # save most recent score\n",
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" eps = max(eps_end, eps_decay*eps) # decrease epsilon\n",
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" print('\\rEpisode {}\\tReward {} \\tAverage Score: {:.2f} \\tEpsilon: {}'.format(i_episode,score,np.mean(scores_window), eps), end=\"\")\n",
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" if i_episode % 100 == 0:\n",
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" print('\\rEpisode {}\\tAverage Score: {:.2f} \\tEpsilon: {}'.format(i_episode, np.mean(scores_window), eps))\n",
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" if i_episode>10 and np.mean(scores[-10:])>450:\n",
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" break\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline\n",
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"plt.plot(scores)\n",
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"plt.title('Scores over increasing episodes')"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Episode 100\tAverage Score: 11.83 \tEpsilon: 0.951217530242334\n",
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"Episode 200\tAverage Score: 16.20 \tEpsilon: 0.9048147898403269\n",
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"Episode 300\tAverage Score: 15.99 \tEpsilon: 0.8606756897186528\n",
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"Episode 400\tAverage Score: 23.94 \tEpsilon: 0.8186898039137951\n",
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"Episode 500\tAverage Score: 26.66 \tEpsilon: 0.7787520933134615\n",
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"Episode 600\tAverage Score: 31.67 \tEpsilon: 0.7407626428726788\n",
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"Episode 700\tAverage Score: 27.90 \tEpsilon: 0.7046264116491338\n",
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"Episode 800\tAverage Score: 39.22 \tEpsilon: 0.6702529950324074\n",
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"Episode 900\tAverage Score: 31.03 \tEpsilon: 0.637556398572254\n",
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"Episode 1000\tAverage Score: 47.30 \tEpsilon: 0.606454822840097\n",
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"Episode 1100\tAverage Score: 30.47 \tEpsilon: 0.5768704587855094\n",
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"Episode 1200\tAverage Score: 26.48 \tEpsilon: 0.548729293075715\n",
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"Episode 1300\tAverage Score: 25.61 \tEpsilon: 0.5219609229311034\n",
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"Episode 1400\tAverage Score: 20.64 \tEpsilon: 0.49649837999353363\n",
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"Episode 1500\tAverage Score: 25.36 \tEpsilon: 0.4722779627867691\n",
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"Episode 1600\tAverage Score: 34.30 \tEpsilon: 0.44923907734991153\n",
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"Episode 1700\tAverage Score: 28.26 \tEpsilon: 0.4273240856451275\n",
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"Episode 1800\tAverage Score: 43.74 \tEpsilon: 0.406478161360422\n",
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"Episode 1900\tAverage Score: 42.58 \tEpsilon: 0.3866491527467055\n",
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"Episode 2000\tAverage Score: 36.56 \tEpsilon: 0.3677874521460121\n",
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"Episode 2100\tAverage Score: 52.12 \tEpsilon: 0.34984587188445015\n",
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"Episode 2200\tAverage Score: 56.85 \tEpsilon: 0.3327795262194029\n",
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"Episode 2300\tAverage Score: 65.41 \tEpsilon: 0.31654571904563433\n",
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"Episode 2400\tAverage Score: 186.93 \tEpsilon: 0.3011038370793723\n",
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"Episode 2500\tAverage Score: 212.44 \tEpsilon: 0.28641524825313086\n",
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"Episode 2600\tAverage Score: 246.87 \tEpsilon: 0.27244320506708813\n",
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"Episode 2700\tAverage Score: 94.46 \tEpsilon: 0.25915275265522114\n",
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"Episode 2800\tAverage Score: 143.46 \tEpsilon: 0.24651064133620196\n",
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"Episode 2900\tAverage Score: 102.21 \tEpsilon: 0.23448524343027585\n",
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"Episode 3000\tAverage Score: 56.00 \tEpsilon: 0.22304647413401948\n",
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"Episode 3100\tAverage Score: 43.23 \tEpsilon: 0.21216571625502262\n",
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"Episode 3200\tAverage Score: 56.39 \tEpsilon: 0.2018157486181985\n",
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"Episode 3300\tAverage Score: 103.49 \tEpsilon: 0.1919706779646106\n",
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"Episode 3400\tAverage Score: 118.43 \tEpsilon: 0.1826058741724434\n",
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"Episode 3500\tAverage Score: 144.03 \tEpsilon: 0.17369790863805412\n",
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"Episode 3509\tReward 500.0 \tAverage Score: 173.83 \tEpsilon: 0.17291782950789983"
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],
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"name": "stdout"
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"Text(0.5, 1.0, 'Scores over increasing episodes')"
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]
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},
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"metadata": {
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||
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"tags": []
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||
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},
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"execution_count": 8
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},
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{
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"output_type": "display_data",
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"tags": [],
|
||
|
|
"needs_background": "light"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "CCoDl_OVpWhl"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
""
|
||
|
|
],
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": []
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|