Assuming a relation such that $y = f(x)$, where $y$ represents a scalar and $x \in 20 \times 1$ vector consisting of zeros and ones, I want to set up a reinforcement learning model that changes the values of the elements of $x$ in order to maximize the $y$.
Let's assume that $y = f(x)$ is equal to
weights = np.random.uniform(-1, 1, 20) y = np.sum(weights*x)
How do I set up such model? In my implementation, I am using keras API and I am trying to adapt the cartpole case code (https://keras.io/examples/rl/actor_critic_cartpole/). However, this code solves for a different problem since the model can perform only one action that can assume discrete values. Secondarily, is it appropriate to set as a reward function simply $y$?. The following code reports how I would structure the architectures.
num_inputs = 20 num_actions = 2 num_hidden = 128 inputs = layers.Input(shape=(num_inputs,)) common = layers.Dense(num_hidden, activation="relu")(inputs) action = layers.Dense(num_actions, activation="softmax")(common) critic = layers.Dense(1)(common) model = keras.Model(inputs=inputs, outputs=[action, critic])
However, how can I control the fact that the input parameters could assume only discrete values (0 and 1)?