mirror of
https://github.com/yunjey/pytorch-tutorial.git
synced 2026-03-13 09:11:37 +08:00
change file name
This commit is contained in:
yunjey
@@ -1,359 +0,0 @@
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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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"source": [
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"# PyTorch DQN Implemenation\n",
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"\n",
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"<br/>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import gym\n",
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"import random\n",
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"import numpy as np\n",
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"import torchvision.transforms as transforms\n",
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"import matplotlib.pyplot as plt\n",
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"from torch.autograd import Variable\n",
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"from collections import deque, namedtuple"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[2017-03-09 21:31:48,174] Making new env: CartPole-v0\n"
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]
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}
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],
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"source": [
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"env = gym.envs.make(\"CartPole-v0\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"class Net(nn.Module):\n",
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" def __init__(self):\n",
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" super(Net, self).__init__()\n",
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" self.fc1 = nn.Linear(4, 128)\n",
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" self.tanh = nn.Tanh()\n",
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" self.fc2 = nn.Linear(128, 2)\n",
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" self.init_weights()\n",
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" \n",
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" def init_weights(self):\n",
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" self.fc1.weight.data.uniform_(-0.1, 0.1)\n",
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" self.fc2.weight.data.uniform_(-0.1, 0.1)\n",
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" \n",
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" def forward(self, x):\n",
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" out = self.fc1(x)\n",
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" out = self.tanh(out)\n",
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" out = self.fc2(out)\n",
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" return out"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def make_epsilon_greedy_policy(network, epsilon, nA):\n",
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" def policy(state):\n",
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" sample = random.random()\n",
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" if sample < (1-epsilon) + (epsilon/nA):\n",
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" q_values = network(state.view(1, -1))\n",
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" action = q_values.data.max(1)[1][0, 0]\n",
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" else:\n",
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" action = random.randrange(nA)\n",
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" return action\n",
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" return policy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"class ReplayMemory(object):\n",
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" \n",
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" def __init__(self, capacity):\n",
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" self.memory = deque()\n",
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" self.capacity = capacity\n",
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" \n",
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" def push(self, transition):\n",
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" if len(self.memory) > self.capacity:\n",
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" self.memory.popleft()\n",
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" self.memory.append(transition)\n",
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" \n",
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" def sample(self, batch_size):\n",
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" return random.sample(self.memory, batch_size)\n",
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" \n",
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" def __len__(self):\n",
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" return len(self.memory)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def to_tensor(ndarray, volatile=False):\n",
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" return Variable(torch.from_numpy(ndarray), volatile=volatile).float()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def deep_q_learning(num_episodes=10, batch_size=100, \n",
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" discount_factor=0.95, epsilon=0.1, epsilon_decay=0.95):\n",
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"\n",
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" # Q-Network and memory \n",
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" net = Net()\n",
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" memory = ReplayMemory(10000)\n",
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" \n",
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" # Loss and Optimizer\n",
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" criterion = nn.MSELoss()\n",
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" optimizer = torch.optim.Adam(net.parameters(), lr=0.001)\n",
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" \n",
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" for i_episode in range(num_episodes):\n",
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" \n",
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" # Set policy (TODO: decaying epsilon)\n",
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" #if (i_episode+1) % 100 == 0:\n",
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" # epsilon *= 0.9\n",
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" \n",
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" policy = make_epsilon_greedy_policy(\n",
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" net, epsilon, env.action_space.n)\n",
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" \n",
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" # Start an episode\n",
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" state = env.reset()\n",
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" \n",
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" for t in range(10000):\n",
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" \n",
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" # Sample action from epsilon greed policy\n",
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" action = policy(to_tensor(state)) \n",
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" next_state, reward, done, _ = env.step(action)\n",
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" \n",
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" \n",
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" # Restore transition in memory\n",
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" memory.push([state, action, reward, next_state])\n",
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" \n",
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" \n",
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" if len(memory) >= batch_size:\n",
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" # Sample mini-batch transitions from memory\n",
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" batch = memory.sample(batch_size)\n",
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" state_batch = np.vstack([trans[0] for trans in batch])\n",
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" action_batch =np.vstack([trans[1] for trans in batch]) \n",
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" reward_batch = np.vstack([trans[2] for trans in batch])\n",
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" next_state_batch = np.vstack([trans[3] for trans in batch])\n",
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" \n",
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" # Forward + Backward + Opimize\n",
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" net.zero_grad()\n",
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" q_values = net(to_tensor(state_batch))\n",
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" next_q_values = net(to_tensor(next_state_batch, volatile=True))\n",
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" next_q_values.volatile = False\n",
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" \n",
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" td_target = to_tensor(reward_batch) + discount_factor * (next_q_values).max(1)[0]\n",
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" loss = criterion(q_values.gather(1, \n",
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" to_tensor(action_batch).long().view(-1, 1)), td_target)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" \n",
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" if done:\n",
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" break\n",
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" \n",
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" state = next_state\n",
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" \n",
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" if len(memory) >= batch_size and (i_episode+1) % 10 == 0:\n",
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" print ('episode: %d, time: %d, loss: %.4f' %(i_episode, t, loss.data[0]))\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"episode: 9, time: 9, loss: 0.9945\n",
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"episode: 19, time: 9, loss: 1.8221\n",
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"episode: 29, time: 9, loss: 4.3124\n",
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"episode: 39, time: 8, loss: 6.9764\n",
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"episode: 49, time: 9, loss: 6.8300\n",
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"episode: 59, time: 8, loss: 5.5186\n",
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"episode: 69, time: 9, loss: 4.1160\n",
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"episode: 79, time: 9, loss: 2.4802\n",
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"episode: 89, time: 13, loss: 0.7890\n",
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"episode: 99, time: 10, loss: 0.2805\n",
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"episode: 109, time: 12, loss: 0.1323\n",
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"episode: 119, time: 13, loss: 0.0519\n",
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"episode: 129, time: 18, loss: 0.0176\n",
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"episode: 139, time: 22, loss: 0.0067\n",
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"episode: 149, time: 17, loss: 0.0114\n",
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"episode: 159, time: 26, loss: 0.0017\n",
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"episode: 169, time: 23, loss: 0.0018\n",
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"episode: 179, time: 21, loss: 0.0023\n",
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"episode: 189, time: 11, loss: 0.0024\n",
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"episode: 199, time: 7, loss: 0.0040\n",
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"episode: 209, time: 8, loss: 0.0030\n",
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"episode: 219, time: 7, loss: 0.0070\n",
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"episode: 229, time: 9, loss: 0.0031\n",
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"episode: 239, time: 9, loss: 0.0029\n",
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"episode: 249, time: 8, loss: 0.0046\n",
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"episode: 259, time: 8, loss: 0.0009\n",
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"episode: 269, time: 10, loss: 0.0020\n",
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"episode: 279, time: 9, loss: 0.0025\n",
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"episode: 289, time: 8, loss: 0.0015\n",
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"episode: 299, time: 10, loss: 0.0009\n",
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"episode: 309, time: 8, loss: 0.0012\n",
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"episode: 319, time: 8, loss: 0.0034\n",
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"episode: 329, time: 8, loss: 0.0008\n",
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"episode: 339, time: 9, loss: 0.0021\n",
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"episode: 349, time: 8, loss: 0.0018\n",
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"episode: 359, time: 9, loss: 0.0017\n",
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"episode: 369, time: 9, loss: 0.0006\n",
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"episode: 379, time: 9, loss: 0.0023\n",
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"episode: 389, time: 10, loss: 0.0017\n",
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"episode: 399, time: 8, loss: 0.0018\n",
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"episode: 409, time: 8, loss: 0.0023\n",
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"episode: 419, time: 9, loss: 0.0020\n",
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"episode: 429, time: 9, loss: 0.0006\n",
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"episode: 439, time: 10, loss: 0.0006\n",
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"episode: 449, time: 10, loss: 0.0025\n",
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"episode: 459, time: 9, loss: 0.0013\n",
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"episode: 469, time: 8, loss: 0.0011\n",
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"episode: 479, time: 8, loss: 0.0005\n",
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"episode: 489, time: 8, loss: 0.0004\n",
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"episode: 499, time: 7, loss: 0.0017\n",
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"episode: 509, time: 7, loss: 0.0004\n",
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"episode: 519, time: 10, loss: 0.0008\n",
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"episode: 529, time: 11, loss: 0.0006\n",
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"episode: 539, time: 9, loss: 0.0010\n",
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"episode: 549, time: 8, loss: 0.0006\n",
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"episode: 559, time: 8, loss: 0.0012\n",
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"episode: 569, time: 9, loss: 0.0011\n",
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"episode: 579, time: 8, loss: 0.0010\n",
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"episode: 589, time: 8, loss: 0.0008\n",
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"episode: 599, time: 10, loss: 0.0010\n",
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"episode: 609, time: 8, loss: 0.0005\n",
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"episode: 619, time: 9, loss: 0.0004\n",
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"episode: 629, time: 8, loss: 0.0007\n",
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"episode: 639, time: 10, loss: 0.0014\n",
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"episode: 649, time: 10, loss: 0.0004\n",
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"episode: 659, time: 9, loss: 0.0008\n",
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"episode: 669, time: 8, loss: 0.0005\n",
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"episode: 679, time: 8, loss: 0.0003\n",
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"episode: 689, time: 9, loss: 0.0009\n",
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"episode: 699, time: 8, loss: 0.0004\n",
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"episode: 709, time: 8, loss: 0.0013\n",
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"episode: 719, time: 8, loss: 0.0006\n",
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"episode: 729, time: 7, loss: 0.0021\n",
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"episode: 739, time: 9, loss: 0.0023\n",
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"episode: 749, time: 9, loss: 0.0039\n",
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"episode: 759, time: 8, loss: 0.0030\n",
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"episode: 769, time: 9, loss: 0.0016\n",
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"episode: 779, time: 7, loss: 0.0041\n",
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"episode: 789, time: 8, loss: 0.0050\n",
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"episode: 799, time: 8, loss: 0.0041\n",
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"episode: 809, time: 11, loss: 0.0053\n",
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"episode: 819, time: 7, loss: 0.0018\n",
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"episode: 829, time: 9, loss: 0.0019\n",
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"episode: 839, time: 11, loss: 0.0017\n",
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"episode: 849, time: 8, loss: 0.0029\n",
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"episode: 859, time: 9, loss: 0.0012\n",
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"episode: 869, time: 9, loss: 0.0036\n",
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"episode: 879, time: 7, loss: 0.0017\n",
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"episode: 889, time: 9, loss: 0.0016\n",
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"episode: 899, time: 10, loss: 0.0023\n",
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"episode: 909, time: 8, loss: 0.0032\n",
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"episode: 919, time: 8, loss: 0.0015\n",
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"episode: 929, time: 9, loss: 0.0021\n",
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"episode: 939, time: 9, loss: 0.0015\n",
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"episode: 949, time: 9, loss: 0.0016\n",
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"episode: 959, time: 9, loss: 0.0013\n",
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"episode: 969, time: 12, loss: 0.0029\n",
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"episode: 979, time: 7, loss: 0.0016\n",
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"episode: 989, time: 7, loss: 0.0012\n",
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"episode: 999, time: 9, loss: 0.0013\n"
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]
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}
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],
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"source": [
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"deep_q_learning(1000)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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||||
"metadata": {
|
||||
"collapsed": true
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||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
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||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 2",
|
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"language": "python",
|
||||
"name": "python2"
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},
|
||||
"language_info": {
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||||
"codemirror_mode": {
|
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"name": "ipython",
|
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.13"
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}
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||||
},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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@@ -1,124 +0,0 @@
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%matplotlib inline
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import torch
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import torch.nn as nn
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import gym
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import random
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import numpy as np
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import torchvision.transforms as transforms
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import matplotlib.pyplot as plt
|
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from torch.autograd import Variable
|
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from collections import deque, namedtuple
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env = gym.envs.make("CartPole-v0")
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class Net(nn.Module):
|
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def __init__(self):
|
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super(Net, self).__init__()
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self.fc1 = nn.Linear(4, 128)
|
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self.tanh = nn.Tanh()
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self.fc2 = nn.Linear(128, 2)
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self.init_weights()
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def init_weights(self):
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self.fc1.weight.data.uniform_(-0.1, 0.1)
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self.fc2.weight.data.uniform_(-0.1, 0.1)
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def forward(self, x):
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out = self.fc1(x)
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out = self.tanh(out)
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out = self.fc2(out)
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return out
|
||||
|
||||
def make_epsilon_greedy_policy(network, epsilon, nA):
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||||
def policy(state):
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sample = random.random()
|
||||
if sample < (1-epsilon) + (epsilon/nA):
|
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q_values = network(state.view(1, -1))
|
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action = q_values.data.max(1)[1][0, 0]
|
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else:
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action = random.randrange(nA)
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return action
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return policy
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||||
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||||
class ReplayMemory(object):
|
||||
|
||||
def __init__(self, capacity):
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self.memory = deque()
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||||
self.capacity = capacity
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||||
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||||
def push(self, transition):
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||||
if len(self.memory) > self.capacity:
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||||
self.memory.popleft()
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self.memory.append(transition)
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def sample(self, batch_size):
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return random.sample(self.memory, batch_size)
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def __len__(self):
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return len(self.memory)
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def to_tensor(ndarray, volatile=False):
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return Variable(torch.from_numpy(ndarray), volatile=volatile).float()
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|
||||
def deep_q_learning(num_episodes=10, batch_size=100,
|
||||
discount_factor=0.95, epsilon=0.1, epsilon_decay=0.95):
|
||||
|
||||
# Q-Network and memory
|
||||
net = Net()
|
||||
memory = ReplayMemory(10000)
|
||||
|
||||
# Loss and Optimizer
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
|
||||
|
||||
for i_episode in range(num_episodes):
|
||||
|
||||
# Set policy (TODO: decaying epsilon)
|
||||
#if (i_episode+1) % 100 == 0:
|
||||
# epsilon *= 0.9
|
||||
|
||||
policy = make_epsilon_greedy_policy(
|
||||
net, epsilon, env.action_space.n)
|
||||
|
||||
# Start an episode
|
||||
state = env.reset()
|
||||
|
||||
for t in range(10000):
|
||||
|
||||
# Sample action from epsilon greed policy
|
||||
action = policy(to_tensor(state))
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
|
||||
|
||||
# Restore transition in memory
|
||||
memory.push([state, action, reward, next_state])
|
||||
|
||||
|
||||
if len(memory) >= batch_size:
|
||||
# Sample mini-batch transitions from memory
|
||||
batch = memory.sample(batch_size)
|
||||
state_batch = np.vstack([trans[0] for trans in batch])
|
||||
action_batch =np.vstack([trans[1] for trans in batch])
|
||||
reward_batch = np.vstack([trans[2] for trans in batch])
|
||||
next_state_batch = np.vstack([trans[3] for trans in batch])
|
||||
|
||||
# Forward + Backward + Opimize
|
||||
net.zero_grad()
|
||||
q_values = net(to_tensor(state_batch))
|
||||
next_q_values = net(to_tensor(next_state_batch, volatile=True))
|
||||
next_q_values.volatile = False
|
||||
|
||||
td_target = to_tensor(reward_batch) + discount_factor * (next_q_values).max(1)[0]
|
||||
loss = criterion(q_values.gather(1,
|
||||
to_tensor(action_batch).long().view(-1, 1)), td_target)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
if done:
|
||||
break
|
||||
|
||||
state = next_state
|
||||
|
||||
if len(memory) >= batch_size and (i_episode+1) % 10 == 0:
|
||||
print ('episode: %d, time: %d, loss: %.4f' %(i_episode, t, loss.data[0]))
|
||||
Reference in New Issue
Block a user