I found him good for discrete space when I ran the project, but I would like to know how to make use of it in continuous space？

[liu-yuntao]

I want to train the agent using the project file after customizing the environment based on the gym's continuous space, the state and actions of the environment are defined as follows：

    self.min_action = np.array([[-3, -3, -3, -3, -3]]).reshape(1,5)
    self.max_action = np.array([[3, 3, 3, 3, 3]]).reshape(1,5)

    self.low_state = np.array(
        [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.float32
    ).reshape(1,10)
    self.high_state = np.array(
        [[50, 50, 50, 50, 50, 300, 300, 300, 300, 300]], dtype=np.float32
    ).reshape(1,10)

    self.action_space = spaces.Box(
        low=self.min_action, high=self.max_action, shape=(1, 5), dtype=np.float32
    )

    self.observation_space = spaces.Box(
        low=self.low_state, high=self.high_state, shape=(1, 10), dtype=np.float32
    )

Is it possible to implement this idea based on PPO ICM? Thanks!