在A3C算法中，可以通过修改策略网络输出的模型架构来实现选择多个动作。例如，可以在策略网络的输出层引入 softmax 函数，使其输出一个概率分布，然后使用 numpy.random.choice 函数选择多个动作。

代码示例：

# 策略网络模型结构
class PolicyNetwork(nn.Module):
    def __init__(self, input_shape, num_actions):
        super(PolicyNetwork, self).__init__()
        self.fc1 = nn.Linear(input_shape, 128)
        self.fc2 = nn.Linear(128, num_actions)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

    # 返回动作概率分布
    def get_action_probs(self, state):
        state = torch.from_numpy(state).float().unsqueeze(0)
        action_logits = self.forward(state)
        action_probs = F.softmax(action_logits, dim=1)
        return action_probs.squeeze().detach().numpy()

# 选择多个动作
def select_actions(action_probs, num_actions, num_select):
    actions = np.zeros(num_select, dtype=int)
    for i in range(num_select):
        # 依据概率分布随机选择一个动作
        actions[i] = np.random.choice(num_actions, p=action_probs)
    return actions

# A3C算法
def train():
    # 初始化环境和策略网络
    env = gym.make('CartPole-v0')
    input_shape = env.observation_space.shape[0]
    num_actions = env.action_space.n
    policy_network = PolicyNetwork(input_shape, num_actions)

    # 迭代训练
    for i in range(num_iterations):
        state = env.reset()
        done = False
        while not done:
            # 获取当前状态下的动作概率分布
            action_probs = policy_network.get_action_probs(state)
            # 选择多个动作
            actions = select_actions(action_probs, num_actions, num