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question:Problem Statement: You are developing a mobile game where players control a spaceship navigating through an asteroid field. The objective is to avoid collisions with the asteroids for as long as possible. The game environment is represented as a 2D grid, where the spaceship can move up, down, left, or right. Objective: Design a deep neural network that takes the current state of the game environment (i.e., the positions of the spaceship and asteroids on the grid) as input and outputs the optimal action (i.e., move up, down, left, or right) to maximize the spaceship's survival time. Additional Information: The game environment is dynamic, with asteroids moving randomly across the grid. The spaceship's movement speed and agility are constant. The reward system is based on the survival time, with higher rewards for longer survival durations. The neural network should use function approximation to learn the optimal policy for navigating the spaceship through the asteroid field. for above problem statement write a complete code doing below task: Implementation should be in OpenAI gym with python. (a) Develop a deep neural network architecture and training procedure that effectively learns the optimal policy for the spaceship to avoid collisions with asteroids and maximize its survival time in the game environment. (i) Environment Setup - Define the game environment, including the state space, action space, rewards, and terminal conditions (e.g., when the spaceship is destroyed). (ii) Replay Buffer - Implement a replay buffer to store experiences (state, action, reward, next state, terminal flag). (iii) Deep Q-Network Architecture: Design the neural network architecture for the DQN using Convolutional Neural Networks. The input to the network is the game state, and the output is the Q-values for each possible action. (iv) Epsilon-Greedy Exploration: Implement an exploration strategy such as epsilon-greedy to balance exploration (trying new actions) and exploitation (using learned knowledge). (v) Training Loop: Initialize the DQN and the target network (a separate network used to stabilize training). In each episode, reset the environment and observe the initial state. While the episode is not done (e.g., spaceship is not destroyed) Select an action using the epsilon-greedy strategy. Execute the action in the environment and observe the next state and reward. Store the experience in the replay buffer. Sample a batch of experiences from the replay buffer. Compute the target Q-values using the Bellman equation. Update the Q-network using back propagation and the loss between predicted and target Q-values. Periodically update the target network weights with the Q-network weights. (vi) Testing and Evaluation: After training, evaluate the DQN by running it in the environment without exploration (set epsilon to 0). Monitor metrics such as average reward per episode, survival time, etc., to assess the performance.
answer:Given the detailed requirements, let's craft a complete solution using PyTorch and OpenAI Gym. This will encompass defining a custom environment in Gym, creating a deep neural network architecture, and implementing reinforcement learning techniques to train the agent. # Prerequisites Ensure you have Python and necessary libraries installed: ```bash pip install torch torchvision numpy gym ``` # (i) Environment Setup We'll start by creating a simple game environment in Gym. Note this example simplifies things for brevity. ```python import gym from gym import spaces import numpy as np class AsteroidFieldEnv(gym.Env): """Custom Environment that follows gym interface""" metadata = {'render.modes': ['human']} def __init__(self, grid_size=10): super(AsteroidFieldEnv, self).__init__() self.grid_size = grid_size self.action_space = spaces.Discrete(4) # Up, down, left, right self.observation_space = spaces.Box(low=0, high=1, shape=(grid_size, grid_size, 1), dtype=np.float32) self.state = None self.end_game = False def reset(self): self.state = np.zeros((self.grid_size, self.grid_size, 1), dtype=np.float32) # Initialize spaceship position (for simplicity, center) self.spaceship_position = [self.grid_size // 2, self.grid_size // 2] self._update_state() return self.state def step(self, action): self._take_action(action) reward = 1.0 # Survival reward done = self.end_game self._update_state() return self.state, reward, done, {} def _take_action(self, action): # Define movement actions if action == 0 and self.spaceship_position[0] > 0: # Up self.spaceship_position[0] -= 1 elif action == 1 and self.spaceship_position[0] < self.grid_size - 1: # Down self.spaceship_position[0] += 1 elif action == 2 and self.spaceship_position[1] > 0: # Left self.spaceship_position[1] -= 1 elif action == 3 and self.spaceship_position[1] < self.grid_size - 1: # Right self.spaceship_position[1] += 1 # For simplicity, let's say hitting the edge ends the game if self.spaceship_position in [[0, 0], [self.grid_size - 1, self.grid_size - 1]]: self.end_game = True def _update_state(self): self.state = np.zeros((self.grid_size, self.grid_size, 1), dtype=np.float32) # Update the spaceship's position in the state self.state[self.spaceship_position[0], self.spaceship_position[1], 0] = 1 def render(self, mode='human'): pass # For brevity, skipping rendering logic ``` # (ii) Replay Buffer A crucial component for experience replay. ```python import random class ReplayBuffer: def __init__(self, capacity): self.capacity = capacity self.buffer = [] self.position = 0 def push(self, state, action, reward, next_state, done): if len(self.buffer) < self.capacity: self.buffer.append(None) self.buffer[self.position] = (state, action, reward, next_state, done) self.position = (self.position + 1) % self.capacity def sample(self, batch_size): return random.sample(self.buffer, batch_size) def __len__(self): return len(self.buffer) ``` # (iii) Deep Q-Network Architecture We'll use PyTorch for the DQN architecture. ```python import torch import torch.nn as nn import torch.nn.functional as F class DQN(nn.Module): def __init__(self, input_dim, output_dim): super(DQN, self).__init__() self.conv1 = nn.Conv2d(input_dim[2], 32, kernel_size=3, stride=1) self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1) self.fc = nn.Linear(64 * (input_dim[0]-4) * (input_dim[1]-4), output_dim) def forward(self, x): x = F.relu(self.conv1(x)) x = F.relu(self.conv2(x)) x = x.view(x.size(0), -1) x = self.fc(x) return x ``` # (iv) Epsilon-Greedy Exploration A function to implement epsilon-greedy exploration: ```python import random def epsilon_greedy(state, policy_net, epsilon, n_actions): if random.random() > epsilon: with torch.no_grad(): return policy_net(state).max(1)[1].view(1, 1) else: return torch.tensor([[random.randrange(n_actions)]], dtype=torch.long) ``` # (v) Training Loop The training procedure using the components defined above: ```python import torch.optim as optim import math from itertools import count def train_dqn(env): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") policy_net = DQN(env.observation_space.shape, env.action_space.n).to(device) target_net = DQN(env.observation_space.shape, env.action_space.n).to(device) target_net.load_state_dict(policy_net.state_dict()) target_net.eval() optimizer = optim.Adam(policy_net.parameters()) memory = ReplayBuffer(10000) # Hyperparameters batch_size = 128 gamma = 0.999 eps_start = 0.9 eps_end = 0.05 eps_decay = 200 num_episodes = 1000 steps_done = 0 for i_episode in range(num_episodes): # Initialize the environment and state state = env.reset() state = torch.tensor([state], device=device, dtype=torch.float32) for t in count(): # Loop till the episode ends epsilon = eps_end + (eps_start - eps_end) * math.exp(-1.0 * steps_done / eps_decay) action = epsilon_greedy(state, policy_net, epsilon, env.action_space.n) next_state, reward, done, _ = env.step(action.item()) reward = torch.tensor([reward], device=device, dtype=torch.float32) next_state = torch.tensor([next_state], device=device, dtype=torch.float32) if done: next_state = None memory.push(state, action, reward, next_state, done) state = next_state steps_done += 1 if len(memory) < batch_size: continue # Sample batch from memory and optimize the model optimize_model(memory, policy_net, target_net, optimizer, batch_size, gamma, device) # Update target net periodically if i_episode % TARGET_UPDATE == 0: target_net.load_state_dict(policy_net.state_dict()) if done: break print(f"Episode {i_episode} completed") # Placeholder for the 'optimize_model' function, which you'll define based on experience replay mechanics def optimize_model(memory, policy_net, target_net, optimizer, batch_size, gamma, device): pass # Implementation of the model optimization logic goes here env = AsteroidFieldEnv() train_dqn(env) ``` # (vi) Testing and Evaluation For the final step, evaluate the trained model by running it in deterministic mode (no exploration) and measure the survival time and rewards. Incorporate the training loop with mechanisms to periodically evaluate the model's performance and tweak the architecture/hyperparameters based on observed outcomes. This given solution provides a high-level outline, focusing on key aspects like the environment setup, DQN architecture, replay buffer, and epsilon-greedy strategy. It requires further development for a fully operational and optimized agent, including the implementation of the `optimize_model` function which is essential for learning from replayed experiences.
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