Mão na massa: identificando objetos com MobileNetV2

!pip install gymnasium matplotlib

import gymnasium as gym
import numpy as np
import random
import matplotlib.pyplot as plt

# Treinamento de agente com Q-Learning:
def train_q_learning(alpha, gamma, epsilon, episodes=2000, test_episodes=100):
    env = gym.make("FrozenLake-v1", is_slippery=True)
    q_table = np.zeros([env.observation_space.n, env.action_space.n])
    
    rewards_per_episode = []
    
    for episode in range(episodes):
        state, _ = env.reset()
        done = False
        total_reward = 0
        
        while not done:
            
            if random.uniform(0, 1) < epsilon:
                action = env.action_space.sample()
            else:
                action = np.argmax(q_table[state])
            
            next_state, reward, done, truncated, info = env.step(action)
            
            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
            total_reward += reward
        
        rewards_per_episode.append(total_reward)
    
    # Avaliação sem exploração:
    successes = 0
    for _ in range(test_episodes):
        state, _ = env.reset()
        done = False
        while not done:
            action = np.argmax(q_table[state])
            state, reward, done, truncated, info = env.step(action)
            if done and reward == 1:
                successes += 1
    
    success_rate = successes / test_episodes
    return rewards_per_episode, success_rate

# Diferentes combinações de hiperparâmetros:
configs = [
    {"alpha":0.8, "gamma":0.95, "epsilon":0.1},
    {"alpha":0.5, "gamma":0.99, "epsilon":0.2},
    {"alpha":0.9, "gamma":0.9, "epsilon":0.05},
    {"alpha":0.7, "gamma":0.8, "epsilon":0.3},
]

results = {}

for i, cfg in enumerate(configs):
    rewards, success_rate = train_q_learning(cfg["alpha"], cfg["gamma"], cfg["epsilon"])
    results[f"Config {i+1}"] = {
        "params": cfg,
        "rewards": rewards,
        "success_rate": success_rate
    }
    print(f"Config {i+1} - alpha={cfg['alpha']}, gamma={cfg['gamma']}, epsilon={cfg['epsilon']}")
    print(f"Taxa de sucesso: {success_rate*100:.2f}%\n")

# Plotagem de recompensas por episódio:
plt.figure(figsize=(12,6))
for key, value in results.items():
    plt.plot(value["rewards"], label=f"{key} {value['params']}")
plt.xlabel("Episódios")
plt.ylabel("Recompensa")
plt.title("Desempenho do agente em diferentes configurações")
plt.legend()
plt.show()


 Requirement already satisfied: gymnasium in /usr/local/lib/python3.12/dist-packages (1.3.0)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)
Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.12/dist-packages (from gymnasium) (2.0.2)
Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from gymnasium) (3.1.2)
Requirement already satisfied: typing-extensions>=4.3.0 in /usr/local/lib/python3.12/dist-packages (from gymnasium) (4.15.0)
Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/lib/python3.12/dist-packages (from gymnasium) (0.0.6)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.63.0)
Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.5.0)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.2)
Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)
Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (2.9.0.post0)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)
Config 1 - alpha=0.8, gamma=0.95, epsilon=0.1
Taxa de sucesso: 0.00%

Config 2 - alpha=0.5, gamma=0.99, epsilon=0.2
Taxa de sucesso: 29.00%

Config 3 - alpha=0.9, gamma=0.9, epsilon=0.05
Taxa de sucesso: 0.00%

Config 4 - alpha=0.7, gamma=0.8, epsilon=0.3
Taxa de sucesso: 4.00%