使用 PaddlePaddle 框架实现行为克隆算法的代码及训练测试代码。

以下是使用PaddlePaddle框架实现行为克隆算法的代码及训练测试代码。

首先，需要安装PaddlePaddle框架和一些必要的依赖库。可以在终端中执行以下命令进行安装：

pip install paddlepaddle==2.0.0
pip install gym
pip install gym[atari]
pip install opencv-python

其中，gym和atari是用于构建游戏环境的依赖库，opencv-python用于对游戏画面进行处理。

接下来，我们来实现行为克隆算法的代码。首先是构建游戏环境和模型的代码：

import gym
import paddle
import paddle.nn as nn
import cv2

# 构建游戏环境
env = gym.make('Pong-v0')

# 构建模型
class PongModel(nn.Layer):
    def __init__(self):
        super(PongModel, self).__init__()
        self.conv1 = nn.Conv2D(in_channels=4, out_channels=32, kernel_size=8, stride=4, padding=0)
        self.conv2 = nn.Conv2D(in_channels=32, out_channels=64, kernel_size=4, stride=2, padding=0)
        self.conv3 = nn.Conv2D(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=0)
        self.fc1 = nn.Linear(in_features=64*7*7, out_features=512)
        self.fc2 = nn.Linear(in_features=512, out_features=env.action_space.n)
        
    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.relu(self.conv2(x))
        x = nn.functional.relu(self.conv3(x))
        x = paddle.flatten(x, start_axis=1)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = PongModel()

这里使用了一个简单的卷积神经网络作为模型，输入是游戏画面的四张连续帧，输出是游戏的动作。接下来是训练代码：

# 定义优化器和损失函数
optimizer = paddle.optimizer.Adam(learning_rate=1e-4, parameters=model.parameters())
criterion = nn.CrossEntropyLoss()

# 训练模型
for i_episode in range(1000):
    observation = env.reset()
    prev_observation = None
    done = False
    total_reward = 0

    while not done:
        # 处理游戏画面
        observation = cv2.cvtColor(observation, cv2.COLOR_RGB2GRAY)
        observation = cv2.resize(observation, (84, 84))
        observation = observation.astype('float32') / 255.0
        if prev_observation is None:
            state = paddle.to_tensor([observation, observation, observation, observation])
        else:
            state = paddle.to_tensor([prev_observation, observation, observation, observation])
        state = paddle.unsqueeze(state, axis=0)

        # 选择动作
        with paddle.no_grad():
            logits = model(state)
            action = paddle.argmax(logits, axis=1).numpy()[0]

        # 执行动作
        observation, reward, done, info = env.step(action+2)
        prev_observation = observation
        total_reward += reward

    # 更新模型
    optimizer.clear_grad()
    loss = criterion(logits, paddle.to_tensor([action]))
    loss.backward()
    optimizer.step()

    # 打印训练信息
    print('Episode {}: Total Reward = {}'.format(i_episode+1, total_reward))

这里使用了类似于Q-learning的方法，每次选择模型预测的最优动作，并执行该动作，然后用损失函数来衡量模型的预测和实际动作的差距，并用优化器来更新模型参数。最后是测试代码：

# 测试模型
observation = env.reset()
prev_observation = None
done = False
total_reward = 0

while not done:
    # 处理游戏画面
    observation = cv2.cvtColor(observation, cv2.COLOR_RGB2GRAY)
    observation = cv2.resize(observation, (84, 84))
    observation = observation.astype('float32') / 255.0
    if prev_observation is None:
        state = paddle.to_tensor([observation, observation, observation, observation])
    else:
        state = paddle.to_tensor([prev_observation, observation, observation, observation])
    state = paddle.unsqueeze(state, axis=0)

    # 选择动作
    with paddle.no_grad():
        logits = model(state)
        action = paddle.argmax(logits, axis=1).numpy()[0]

    # 执行动作
    observation, reward, done, info = env.step(action+2)
    prev_observation = observation
    total_reward += reward

# 打印测试结果
print('Total Reward = {}'.format(total_reward))

这里的测试代码与训练代码基本相同，只是没有更新模型参数，而是用模型来预测动作并执行动作，最后输出总得分。