def test_icm():
    """
    **Overview**:
        This function serves as an end-to-end testing routine for the Intrinsic Curiosity Module (ICM) model. The test includes the following steps:

        1. Initialization of the ICM model with specified dimensions for the states, actions, and hidden layers.
        2. Generation of synthetic data to simulate states, actions, and next states.
        3. Training of the ICM model using generated data and the Adam optimizer.
        4. Evaluation of the trained model's performance based on its prediction loss.
        5. Computation of the ICM intrinsic reward based on the model's prediction error.

        The function is intended for debugging and performance assessment of the ICM model in a controlled setting with synthetic data.
    """

    # Define the dimensions for the states, actions, and hidden layer.
    # These numbers correspond to the size of the input that the ICM model expects.
    obs_dim = 10
    action_dim = 5
    hidden_dim = 20

    # Initialize the ICM model and the optimizer.
    # The ICM model is used to predict the next state of the environment given the current state and action.
    # The optimizer is used to optimize the parameters of the model to minimize the prediction error.
    model = ICMNetwork(obs_dim, action_dim, hidden_dim)
    optimizer = optim.Adam(model.parameters())

    # Generate some random data to train the ICM model.
    # 'states' and 'next_states' represent the current and next states of the environment.
    # 'actions' represent the actions taken by the agent.
    states = torch.randn(100, obs_dim)
    next_states = torch.randn(100, obs_dim)
    actions = torch.randn(100, action_dim)

    # Train the model using the generated data.
    # The 'train' function updates the parameters of the model to minimize the prediction error.
    train_loss = train(model, optimizer, states, actions, next_states)
    print(f"Train loss: {train_loss}")

    # Evaluate the model using the same data.
    # The 'eval' function computes the prediction error without updating the model parameters.
    eval_loss = eval(model, states, actions, next_states)
    print(f"Eval loss: {eval_loss}")

    # Test the computation of the ICM intrinsic reward.
    # The 'compute_icm_reward' function computes the reward based on the prediction error of the model.
    compute_icm_reward(model, states, actions, next_states)

    **Overview**:
        This function serves as an end-to-end testing routine for the Intrinsic Curiosity Module (ICM) model. The test includes the following steps:

        1. Initialization of the ICM model with specified dimensions for the states, actions, and hidden layers.
        2. Generation of synthetic data to simulate states, actions, and next states.
        3. Training of the ICM model using generated data and the Adam optimizer.
        4. Evaluation of the trained model's performance based on its prediction loss.
        5. Computation of the ICM intrinsic reward based on the model's prediction error.

        The function is intended for debugging and performance assessment of the ICM model in a controlled setting with synthetic data.
    
chapter4_reward/curiosity_icm.py