用代码生成无人机强化学习在airsim避障

首先，需要安装 AirSim 模拟器和 Python API。可以参考官方文档进行安装。

接下来，需要定义无人机的状态和动作。在本例中，我们将无人机状态定义为无人机的位置和速度。动作定义为无人机的油门、俯仰角和偏航角。

class DroneState:
    def __init__(self, pos, vel):
        self.pos = pos
        self.vel = vel

class DroneAction:
    def __init__(self, throttle, pitch, yaw):
        self.throttle = throttle
        self.pitch = pitch
        self.yaw = yaw

然后，需要定义一个强化学习代理。在本例中，我们使用深度 Q 学习算法（DQN）作为代理。DQN 是一种深度强化学习算法，它使用深度神经网络来估计 Q 值函数，并通过贪心策略选择动作。

class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = deque(maxlen=2000)
        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 np.random.uniform(-1, 1, size=(self.action_size,))
        act_values = self.model.predict(state)
        return 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)

在训练过程中，我们需要定义奖励函数。在本例中，我们将奖励定义为无人机与障碍物的距离的负值，以鼓励无人机尽可能远离障碍物。

def get_reward(prev_state, next_state):
    dist_to_obstacle = np.linalg.norm(next_state.pos - obstacle_pos)
    reward = -(dist_to_obstacle / 10)
    return reward

最后，我们可以使用以下代码来训练代理并测试它的性能。

# 初始化无人机和障碍物的位置
drone_pos = np.array([0, 0, -10])
drone_vel = np.array([0, 0, 0])
drone_state = DroneState(drone_pos, drone_vel)
obstacle_pos = np.array([10, 10, -10])

# 初始化代理
state_size = 6
action_size = 3
agent = DQNAgent(state_size, action_size)

# 训练代理
batch_size = 32
num_episodes = 1000
for i in range(num_episodes):
    state = np.concatenate((drone_state.pos, drone_state.vel))
    for t in range(100):
        # 获取动作
        action = agent.act(state)
        drone_action = DroneAction(*action)

        # 更新无人机状态
        drone_pos, drone_vel = update_drone_state(drone_state, drone_action)
        drone_state = DroneState(drone_pos, drone_vel)

        # 获取奖励并更新经验回放
        next_state = np.concatenate((drone_state.pos, drone_state.vel))
        reward = get_reward(state, next_state)
        agent.remember(state, action, reward, next_state, done)
        state = next_state

        if done:
            break

    # 更新 Q 值函数
    if len(agent.memory) > batch_size:
        agent.replay(batch_size)

    # 保存模型
    if i % 50 == 0:
        agent.save('dqn_model.h5')

# 测试代理
drone_state = DroneState(drone_pos, drone_vel)
for t in range(100):
    state = np.concatenate((drone_state.pos, drone_state.vel))
    action = agent.act(state)
    drone_action = DroneAction(*action)
    drone_pos, drone_vel = update_drone_state(drone_state, drone_action)
    drone_state = DroneState(drone_pos, drone_vel)
    if done:
        break

在这个例子中，我们使用了 DQN 算法来训练代理。你也可以尝试其他强化学习算法，例如 actor-critic 算法或者 policy gradient 算法。此外，你还可以尝试使用更高级的深度神经网络架构来提高代理的性能。