answer:Чтобы реализовать агента Deep Q-Learning (DQN) с использованием PyTorch, необходимо выполнить несколько шагов. DQN — это метод, который объединяет идеи Q-Learning с глубоким обучением, чтобы обучить агентов решать задачи принятия решений на основе изображений или иных высоко-размерных данных. В этом примере будет создан очень простой DQN агент для игры в среде gym, такой как `CartPole-v1`. # Шаги для создания DQN: 1. **Импорт необходимых библиотек:** ```python import gym import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import numpy as np import random from collections import deque ``` 2. **Определение нейронной сети для DQN:** Создадим нейросеть, которая будет работать как функция оценки Q-значений: ```python class DQN(nn.Module): def __init__(self, input_dim, output_dim): super(DQN, self).__init__() self.fc1 = nn.Linear(input_dim, 128) self.fc2 = nn.Linear(128, 256) self.fc3 = nn.Linear(256, output_dim) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) return self.fc3(x) ``` 3. **Реализация агента DQN:** Теперь создадим агента, который будет использовать нашу модель для определения и обновления политик: ```python class DQNAgent: def __init__(self, state_size, action_size, learning_rate=0.001, gamma=0.99, memory_size=10000, batch_size=64): self.state_size = state_size self.action_size = action_size self.memory = deque(maxlen=memory_size) self.batch_size = batch_size self.gamma = gamma self.learning_rate = learning_rate self.model = DQN(state_size, action_size) self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate) def remember(self, state, action, reward, next_state, done): """Сохранение эксперимента""" self.memory.append((state, action, reward, next_state, done)) def act(self, state, epsilon=0.01): """Возвращение действия: случайно или с использованием политики""" if random.random() <= epsilon: return random.randrange(self.action_size) state = torch.FloatTensor(state).unsqueeze(0) q_values = self.model(state).detach() return np.argmax(q_values.numpy()) def replay(self): """Обучение на случайных мини-пакетах из памяти""" if len(self.memory) < self.batch_size: return minibatch = random.sample(self.memory, self.batch_size) states, actions, rewards, next_states, dones = zip(*minibatch) states = torch.FloatTensor(states) actions = torch.LongTensor(actions) rewards = torch.FloatTensor(rewards) next_states = torch.FloatTensor(next_states) dones = torch.FloatTensor(dones) # Получаем предсказанные Q-значения для выполненных действий q_values = self.model(states).gather(1, actions.unsqueeze(-1)).squeeze(-1) # Вычисляем предельные Q-значения для следующих состояний с помощью нейросети next_q_values = self.model(next_states).max(1)[0].detach() expected_q_values = rewards + self.gamma * next_q_values * (1 - dones) loss = F.mse_loss(q_values, expected_q_values) self.optimizer.zero_grad() loss.backward() self.optimizer.step() ``` 4. **Обучение DQN агента:** Наконец, обучим нашего агента в среде `CartPole-v1`: ```python env = gym.make('CartPole-v1') state_size = env.observation_space.shape[0] action_size = env.action_space.n agent = DQNAgent(state_size, action_size) episodes = 1000 for e in range(episodes): state = env.reset() state = np.reshape(state, [1, state_size]) for t in range(500): # Максимальное количество шагов action = agent.act(state) next_state, reward, done, _ = env.step(action) reward = reward if not done else -10 next_state = np.reshape(next_state, [1, state_size]) agent.remember(state, action, reward, next_state, done) state = next_state if done: print(f"Эпизод: {e+1}/{episodes}, Счет: {t+1}") break agent.replay() ``` Этот код демонстрирует основы создания и обучения DQN агента. В реальных проектах стоит добавить функции, такие как сохранение и загрузка моделей, тонкая настройка гиперпараметров, и внедрение дополнительных усовершенствований DQN, таких как Double DQN, Dueling DQN, или Prioritized Experience Replay.

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answer:/imagine prompt: Dragon Ball Z anime character Goku, portrait shot from the waist up, minimalist background, child-like illustration style, showcasing Goku's iconic spiky hair, orange gi with blue belt, intense look in his eyes, surrounded by a halo of energy, exuding strength and power, captured with a Canon EOS R camera, 50mm lens, composition centered on Goku's face to capture his determination and fighting spirit, resembling a children's storybook illustration. --ar 1:1 --v 5.2 /imagine prompt: Goku from Dragon Ball Z, full-body shot, in a serene garden setting with blooming cherry blossom trees, gentle breeze rustling his outfit, soft pastel colors in the background, a peaceful and meditative atmosphere, capturing Goku's calm and focused expression, highlighting the intricate details of his martial arts attire, Illustration, watercolor painting, --ar 9:16 --v 5 /imagine prompt: Goku in a futuristic cityscape, surrounded by towering skyscrapers and neon lights, showcasing his superhuman abilities, mid-air pose with energy beams radiating from his hands, blending elements of sci-fi and martial arts, digital art, cyberpunk style, --ar 16:9 --v 5 /imagine prompt: Goku facing off against a powerful adversary on a distant planet, rocky terrain with swirling dust clouds, intense battle scene with energy blasts and shockwaves, dynamic composition capturing the action and adrenaline, dark and dramatic atmosphere, emphasizing the high-stakes confrontation, 3D rendering, cinematic style, --ar 16:9 --v 5.2

answer:# Independent Programmer Revives Nostalgia with Edgy Wii Game: Wii Are Resorting to Violence **August 25, 2023** – In a refreshing twist to the Wii game lineup, a talented and somewhat rebellious coder, Nathan Rodriguez, has taken the gaming world by surprise with his latest creation: *Wii Are Resorting to Violence*. This bold title brings a unique blend of humor, action, and a pinch of dark comedy to the classic Wii, a console beloved by millions for its family-friendly games and innovative motion controls. Despite the Wii’s discontinuation years ago, the console remains popular among retro gaming enthusiasts and families alike. Rodriguez’s game taps into this nostalgia, offering a unique experience that diverges significantly from the console's typical fare. *Wii Are Resorting to Violence* is described by its creator as “a love letter to the Wii, with a twist nobody saw coming.” The game parodies the popular *Wii Sports Resort*, transforming its serene island setting into a chaotic battleground where players engage in over-the-top combat sports. From fencing with baguettes to dodgeball using watermelons, Rodriguez's game injects a dose of absurdity and wit into the Wii’s game library. "I grew up playing the Wii, and it's always had a special place in my heart," Rodriguez shared in an interview. "With *Wii Are Resorting to Violence*, I wanted to pay homage to those memories while also making something that stands out as entirely its own beast. It's been a thrilling challenge to develop this game, ensuring that it resonates with long-time fans of the console and newcomers looking for something out of the ordinary." Rodriguez's approach to game development was unorthodox. Leveraging open-source tools and a deep understanding of the Wii’s hardware, he single-handedly crafted the game’s mechanics, art, and programming. The game’s announcement was initially met with skepticism, but upon release, it quickly gained a following, praised for its ingenuity, humor, and how it breathes new life into the Wii’s game catalog. Critics and gamers alike have lauded *Wii Are Resorting to Violence* for its creative gameplay, tight controls, and how it manages to maintain a balance between reverence for its source material and its unique, edgy flavor. “It’s rare to see such a daring game come out for a console that’s been out of the spotlight for so long,” remarked Alex Chen, editor of the online gaming magazine Pixelated. “Rodriguez has not only paid a fitting tribute to the Wii but has also shown that there's still room for innovation and surprise in the world of gaming.” Gaming forums and social media platforms are abuzz with discussions about the game, with many users sharing their favorite moments and the hilariously unexpected ways they’ve found to defeat their opponents. Beyond its entertainment value, *Wii Are Resorting to Violence* has sparked conversations about the potential for indie games to revitalize interest in older gaming systems. Rodriguez has expressed his astonishment at the game's positive reception and hinted at possible future projects. “The response has been overwhelmingly positive, and it's inspired me to explore what else might be possible,” he stated, leaving many wondering what the future might hold for this innovative developer and the beloved Wii console. *Wii Are Resorting to Violence* is available in limited quantities, mainly sold through Rodriguez’s website, and has quickly become a sought-after collector's item for fans of the console and indie games. As Rodriguez continues to pave his path in the gaming industry, his debut title stands as a testament to the enduring appeal of the Wii and the limitless creativity of indie game developers.