强化学习 自动驾驶 carla

强化学习是一种机器学习方法，它通过试错来学习如何在特定环境中采取行动以最大化奖励。CARLA是一个开源的自动驾驶仿真平台，可以用于测试和评估自动驾驶算法。下面是使用强化学习在CARLA中实现自动驾驶的一些步骤：

1. 安装CARLA和Python API

   # 安装CARLA
   wget https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/CARLA_0.9.11.tar.gz
   tar -xvf CARLA_0.9.11.tar.gz
   # 安装Python API
   pip install pygame numpy networkx scipy matplotlib
   git clone https://github.com/carla-simulator/carla.git
   cd carla/PythonAPI/carla/dist
   easy_install carla-0.9.11-py3.7-linux-x86_64.egg

2. 创建CARLA环境

   import carla
   
   # 连接到CARLA服务器
   client = carla.Client('localhost', 2000)
   client.set_timeout(10.0)
   
   # 获取CARLA世界
   world = client.get_world()
   
   # 设置天气和时间
   weather = carla.WeatherParameters(cloudiness=10.0, precipitation=10.0, sun_altitude_angle=70.0)
   world.set_weather(weather)
   world.set_sun_position(carla.Location(x=0.0, y=0.0, z=0.0))
   
   # 创建车辆和摄像头
   blueprint_library = world.get_blueprint_library()
   vehicle_bp = blueprint_library.filter('vehicle.tesla.model3')[0]
   spawn_point = carla.Transform(carla.Location(x=50.0, y=0.0, z=2.0), carla.Rotation(yaw=180.0))
   vehicle = world.spawn_actor(vehicle_bp, spawn_point)
   camera_bp = blueprint_library.find('sensor.camera.rgb')
   camera_transform = carla.Transform(carla.Location(x=1.5, z=2.4))
   camera = world.spawn_actor(camera_bp, camera_transform, attach_to=vehicle)

3. 实现强化学习算法
这里我们以Deep Q-Network (DQN)为例，使用Keras实现神经网络。

   import keras
   from keras.models import Sequential
   from keras.layers import Dense, Flatten
   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 = 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(Flatten(input_shape=(1,) + self.state_size))
           model.add(Dense(24, 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
   
   # 初始化DQNAgent
   state_size = (800, 600, 3)
   action_size = 3
   agent = DQNAgent(state_size, action_size)
   
   # 训练DQNAgent
   for e in range(EPISODES):
       state = env.reset()
       state = np.reshape(state, [1, 1] + list(state_size))
       for time in range(500):
           action = agent.act(state)
           next_state, reward, done, _ = env.step(action)
           next_state = np.reshape(next_state, [1, 1] + list(state_size))
           agent.remember(state, action, reward, next_state, done)
           state = next_state
           if done:
               break
           if len(agent.memory) > batch_size:
               agent.replay(batch_size)

4. 运行CARLA仿真

   ./CarlaUE4.sh -windowed -carla-server -benchmark -fps=20