app.py

# app.py

from flask import Flask, jsonify, render_template
import numpy as np

app = Flask(__name__)

# Define the maze
maze = np.array([
    [0, 1, 0, 0, 0],
    [0, 1, 0, 1, 0],
    [0, 0, 0, 1, 0],
    [0, 1, 1, 1, 0],
    [0, 0, 0, 0, 0]
])

# Define the target
target = (4, 4)

# Initialize the agent's position
agent_position = (0, 0)

# Define the Q-table
q_table = np.zeros((5, 5, 4))  # 5x5 grid, 4 actions (up, down, left, right)

# Define the reward function
def get_reward(agent_position):
    if agent_position == target:
        return 100
    elif maze[agent_position] == 1:
        return -10
    return -1

# Define the action space
actions = {
    0: (-1, 0),  # up
    1: (1, 0),   # down
    2: (0, -1),  # left
    3: (0, 1)   # right
}

# Define the training function
def train_q_table(episodes=1000, alpha=0.1, gamma=0.95, epsilon=0.1):
    global q_table, agent_position

    for _ in range(episodes):
        state = (0, 0)
        while state != target:
            if np.random.uniform(0, 1) < epsilon:
                action = np.random.choice([0, 1, 2, 3])
            else:
                action = np.argmax(q_table[state])

            next_state = tuple(np.array(state) + np.array(actions[action]))

            if next_state[0] < 0 or next_state[0] >= 5 or next_state[1] < 0 or next_state[1] >= 5:
                next_state = state  # Stay in the same state if the move is invalid

            reward = get_reward(next_state)
            old_value = q_table[state + (action,)]
            next_max = np.max(q_table[next_state])

            new_value = (1 - alpha) * old_value + alpha * (reward + gamma * next_max)
            q_table[state + (action,)] = new_value

            state = next_state

    agent_position = (0, 0)

# Train the Q-table
train_q_table()

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/step')
def step():
    global agent_position

    action = np.argmax(q_table[agent_position])
    next_state = tuple(np.array(agent_position) + np.array(actions[action]))

    if next_state[0] < 0 or next_state[0] >= 5 or next_state[1] < 0 or next_state[1] >= 5:
        next_state = agent_position  # Stay in the same state if the move is invalid

    agent_position = next_state

    # Convert NumPy arrays to lists to ensure JSON serializability
    return jsonify({
        'agent_position': list(agent_position),
        'target': list(target),
        'maze': maze.tolist()
    })

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860)