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深層強化学習 CartPole-v0 を動かしてみる(PyTorch のサンプルプログラムを使用)

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ユースケース: 深層強化学習 CartPole-v0 を動かしてみたい

次のWebページに記載のソースコード(単純な CNN を用いた画像分類)を実行してみる

参考 Web ページ: https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html

先人に感謝

PyTorch の Web ページ: http://pytorch.org

GitHub の PyTorch の Webページ: https://github.com/pytorch/pytorch


前準備

PyTorch のインストール

Open-AI gym のインストール


深層強化学習 CartPole-v0 を動かしてみる(PyTorch のサンプルプログラムを使用)

Python プログラムを動かしたい. そのために,「Python コンソール」を使う.

PyCharmspyder を使うのが簡単.

  1. インポート

    import gym
    import math
    import random
    import numpy as np
    import matplotlib
    import matplotlib.pyplot as plt
    from collections import namedtuple
    from itertools import count
    from PIL import Image
    
    import torch
    import torch.nn as nn
    import torch.optim as optim
    import torch.nn.functional as F
    import torchvision.transforms as T
    
    
    env = gym.make('CartPole-v0').unwrapped
    
    # set up matplotlib
    is_ipython = 'inline' in matplotlib.get_backend()
    if is_ipython:
        from IPython import display
    
    plt.ion()
    
    # if gpu is to be used
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    

    Ubuntu での実行結果例

  2. CIFAR 10 のダウンロード

    transform = transforms.Compose(
        [transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
    
    trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
    
    testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
    
    classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
    

    Ubuntu の PyCharm での実行結果例

  3. Replay Memory の作成

    Transition = namedtuple('Transition',
                            ('state', 'action', 'next_state', 'reward'))
    
    
    class ReplayMemory(object):
    
        def __init__(self, capacity):
            self.capacity = capacity
            self.memory = []
            self.position = 0
    
        def push(self, *args):
            """Saves a transition."""
            if len(self.memory) < self.capacity:
                self.memory.append(None)
            self.memory[self.position] = Transition(*args)
            self.position = (self.position + 1) % self.capacity
    
        def sample(self, batch_size):
            return random.sample(self.memory, batch_size)
    
        def __len__(self):
            return len(self.memory)
    

  4. Q-network の作成

    class DQN(nn.Module):
    
        def __init__(self, h, w, outputs):
            super(DQN, self).__init__()
            self.conv1 = nn.Conv2d(3, 16, kernel_size=5, stride=2)
            self.bn1 = nn.BatchNorm2d(16)
            self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2)
            self.bn2 = nn.BatchNorm2d(32)
            self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2)
            self.bn3 = nn.BatchNorm2d(32)
    
            # Number of Linear input connections depends on output of conv2d layers
            # and therefore the input image size, so compute it.
            def conv2d_size_out(size, kernel_size = 5, stride = 2):
                return (size - (kernel_size - 1) - 1) // stride  + 1
            convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w)))
            convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h)))
            linear_input_size = convw * convh * 32
            self.head = nn.Linear(linear_input_size, outputs)
    
        # Called with either one element to determine next action, or a batch
        # during optimization. Returns tensor([[left0exp,right0exp]...]).
        def forward(self, x):
            x = F.relu(self.bn1(self.conv1(x)))
            x = F.relu(self.bn2(self.conv2(x)))
            x = F.relu(self.bn3(self.conv3(x)))
            return self.head(x.view(x.size(0), -1))
    

  5. Input Extraction (入力の抽出)

    resize = T.Compose([T.ToPILImage(),
                        T.Resize(40, interpolation=Image.CUBIC),
                        T.ToTensor()])
    
    
    def get_cart_location(screen_width):
        world_width = env.x_threshold * 2
        scale = screen_width / world_width
        return int(env.state[0] * scale + screen_width / 2.0)  # MIDDLE OF CART
    
    def get_screen():
        # Returned screen requested by gym is 400x600x3, but is sometimes larger
        # such as 800x1200x3. Transpose it into torch order (CHW).
        screen = env.render(mode='rgb_array').transpose((2, 0, 1))
        # Cart is in the lower half, so strip off the top and bottom of the screen
        _, screen_height, screen_width = screen.shape
        screen = screen[:, int(screen_height*0.4):int(screen_height * 0.8)]
        view_width = int(screen_width * 0.6)
        cart_location = get_cart_location(screen_width)
        if cart_location < view_width // 2:
            slice_range = slice(view_width)
        elif cart_location > (screen_width - view_width // 2):
            slice_range = slice(-view_width, None)
        else:
            slice_range = slice(cart_location - view_width // 2,
                                cart_location + view_width // 2)
        # Strip off the edges, so that we have a square image centered on a cart
        screen = screen[:, :, slice_range]
        # Convert to float, rescale, convert to torch tensor
        # (this doesn't require a copy)
        screen = np.ascontiguousarray(screen, dtype=np.float32) / 255
        screen = torch.from_numpy(screen)
        # Resize, and add a batch dimension (BCHW)
        return resize(screen).unsqueeze(0).to(device)
    
    
    env.reset()
    plt.figure()
    plt.imshow(get_screen().cpu().squeeze(0).permute(1, 2, 0).numpy(),
               interpolation='none')
    plt.title('Example extracted screen')
    plt.show()
    

  6. ハイパーパラメータなど

    BATCH_SIZE = 128
    GAMMA = 0.999
    EPS_START = 0.9
    EPS_END = 0.05
    EPS_DECAY = 200
    TARGET_UPDATE = 10
    
    # Get screen size so that we can initialize layers correctly based on shape
    # returned from AI gym. Typical dimensions at this point are close to 3x40x90
    # which is the result of a clamped and down-scaled render buffer in get_screen()
    init_screen = get_screen()
    _, _, screen_height, screen_width = init_screen.shape
    
    # Get number of actions from gym action space
    n_actions = env.action_space.n
    
    policy_net = DQN(screen_height, screen_width, n_actions).to(device)
    target_net = DQN(screen_height, screen_width, n_actions).to(device)
    target_net.load_state_dict(policy_net.state_dict())
    target_net.eval()
    
    optimizer = optim.RMSprop(policy_net.parameters())
    memory = ReplayMemory(10000)
    
    
    steps_done = 0
    
    
    def select_action(state):
        global steps_done
        sample = random.random()
        eps_threshold = EPS_END + (EPS_START - EPS_END) * \
            math.exp(-1. * steps_done / EPS_DECAY)
        steps_done += 1
        if sample > eps_threshold:
            with torch.no_grad():
                # t.max(1) will return largest column value of each row.
                # second column on max result is index of where max element was
                # found, so we pick action with the larger expected reward.
                return policy_net(state).max(1)[1].view(1, 1)
        else:
            return torch.tensor([[random.randrange(n_actions)]], device=device, dtype=torch.long)
    
    
    episode_durations = []
    
    
    def plot_durations():
        plt.figure(2)
        plt.clf()
        durations_t = torch.tensor(episode_durations, dtype=torch.float)
        plt.title('Training...')
        plt.xlabel('Episode')
        plt.ylabel('Duration')
        plt.plot(durations_t.numpy())
        # Take 100 episode averages and plot them too
        if len(durations_t) >= 100:
            means = durations_t.unfold(0, 100, 1).mean(1).view(-1)
            means = torch.cat((torch.zeros(99), means))
            plt.plot(means.numpy())
    
        plt.pause(0.001)  # pause a bit so that plots are updated
        if is_ipython:
            display.clear_output(wait=True)
            display.display(plt.gcf())
    

  7. 最適化の1ステップを実行する関数

    def optimize_model():
        if len(memory) < BATCH_SIZE:
            return
        transitions = memory.sample(BATCH_SIZE)
        # Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for
        # detailed explanation). This converts batch-array of Transitions
        # to Transition of batch-arrays.
        batch = Transition(*zip(*transitions))
    
        # Compute a mask of non-final states and concatenate the batch elements
        # (a final state would've been the one after which simulation ended)
        non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,
                                              batch.next_state)), device=device, dtype=torch.uint8)
        non_final_next_states = torch.cat([s for s in batch.next_state
                                                    if s is not None])
        state_batch = torch.cat(batch.state)
        action_batch = torch.cat(batch.action)
        reward_batch = torch.cat(batch.reward)
    
        # Compute Q(s_t, a) - the model computes Q(s_t), then we select the
        # columns of actions taken. These are the actions which would've been taken
        # for each batch state according to policy_net
        state_action_values = policy_net(state_batch).gather(1, action_batch)
    
        # Compute V(s_{t+1}) for all next states.
        # Expected values of actions for non_final_next_states are computed based
        # on the "older" target_net; selecting their best reward with max(1)[0].
        # This is merged based on the mask, such that we'll have either the expected
        # state value or 0 in case the state was final.
        next_state_values = torch.zeros(BATCH_SIZE, device=device)
        next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach()
        # Compute the expected Q values
        expected_state_action_values = (next_state_values * GAMMA) + reward_batch
    
        # Compute Huber loss
        loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))
    
        # Optimize the model
        optimizer.zero_grad()
        loss.backward()
        for param in policy_net.parameters():
            param.grad.data.clamp_(-1, 1)
        optimizer.step()
    

  8. 訓練(学習)

    num_episodes = 50
    for i_episode in range(num_episodes):
        # Initialize the environment and state
        env.reset()
        last_screen = get_screen()
        current_screen = get_screen()
        state = current_screen - last_screen
        for t in count():
            # Select and perform an action
            action = select_action(state)
            _, reward, done, _ = env.step(action.item())
            reward = torch.tensor([reward], device=device)
    
            # Observe new state
            last_screen = current_screen
            current_screen = get_screen()
            if not done:
                next_state = current_screen - last_screen
            else:
                next_state = None
    
            # Store the transition in memory
            memory.push(state, action, next_state, reward)
    
            # Move to the next state
            state = next_state
    
            # Perform one step of the optimization (on the target network)
            optimize_model()
            if done:
                episode_durations.append(t + 1)
                plot_durations()
                break
        # Update the target network, copying all weights and biases in DQN
        if i_episode % TARGET_UPDATE == 0:
            target_net.load_state_dict(policy_net.state_dict())
    
    print('Complete')
    env.render()
    env.close()
    plt.ioff()
    plt.show()