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```python import numpy as np import random class GridWorld: def __init__(self, size=5, obstacle_positions=None, goal_position=(4, 4)): self.size = size self.state = (0, 0) # Starting position self.goal_position = goal_position self.obstacle_positions = obstacle_positions if obstacle_positions else [] self.actions = ["up", "down", "left", "right"] def reset(self): self.state = (0, 0) return self.state def step(self, action): row, col = self.state if action == "up": new_row = max(0, row - 1) new_col = col elif action == "down": new_row = min(self.size - 1, row + 1) new_col = col elif action == "left": new_row = row new_col = max(0, col - 1) elif action == "right": new_row = row new_col = min(self.size - 1, col + 1) else: raise ValueError("Invalid action") new_state = (new_row, new_col) if new_state in self.obstacle_positions: reward = -10 # Penalty for hitting an obstacle elif new_state == self.goal_position: reward = 10 # Reward for reaching the goal else: reward = -1 # small penalty to encourage shorter paths self.state = new_state done = (new_state == self.goal_position) return new_state, reward, done def q_learning(env, alpha=0.1, gamma=0.9, epsilon=0.1, num_episodes=1000): q_table = np.zeros((env.size, env.size, len(env.actions))) for episode in range(num_episodes): state = env.reset() done = False while not done: # Epsilon-greedy action selection if random.uniform(0, 1) < epsilon: action = random.choice(env.actions) else: action_index = np.argmax(q_table[state[0], state[1]]) action = env.actions[action_index] # Take action and observe next_state, reward, done = env.step(action) # Update Q-value action_index = env.actions.index(action) best_next_q = np.max(q_table[next_state[0], next_state[1]]) q_table[state[0], state[1], action_index] += alpha * (reward + gamma * best_next_q - q_table[state[0], state[1], action_index]) # Update state state = next_state return q_table # Example usage env = GridWorld(size=5, obstacle_positions=[(1, 1), (2, 3)]) q_table = q_learning(env) print("Learned Q-table:") print(q_table) # Example of using the Q-table to navigate the environment state = env.reset() done = False path = [state] while not done: action_index = np.argmax(q_table[state[0], state[1]]) action = env.actions[action_index] state, reward, done = env.step(action) path.append(state) print("Optimal path:", path) ```ã³ãŒãã®èª¬æïŒ
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