强化学习自动驾驶规划

强化学习可以用于自动驾驶规划。以下是一个基于深度Q-Learning的自动驾驶汽车的示例程序：

import numpy as np
import random
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = []
        self.gamma = 0.95
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()

    def _build_model(self):
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model
    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])

    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = (reward + self.gamma *
                          np.amax(self.model.predict(next_state)[0]))
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

    def load(self, name):
        self.model.load_weights(name)

    def save(self, name):
        self.model.save_weights(name)

if __name__ == "__main__":
    state_size = 4
    action_size = 2
    agent = DQNAgent(state_size, action_size)
    done = False
    batch_size = 32

    for e in range(1000):
        state = env.reset()
        state = np.reshape(state, [1, state_size])
        for time in range(500):
            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("episode: {}/{}, score: {}, e: {:.2}"
                      .format(e, 1000, time, agent.epsilon))
                break
        if len(agent.memory) > batch_size:
            agent.replay(batch_size)
        if e % 50 == 0:
            agent.save("cartpole-dqn.h5")

该程序使用DQNAgent类来实现深度Q-Learning算法。它使用一个神经网络来估计每个状态下每个动作的Q值。在每个时间步，代理根据当前状态选择一个动作，并接收一个奖励。代理将状态，动作，奖励和下一个状态存储在记忆中，并使用这些经验来更新神经网络的权重。在每个时间步，代理还会减少其epsilon值，以便逐渐减少随机动作的数量。