I have an environment with 4 objects in it. All of these objects can either be selected or not selected. So the actions taken by my DQN should look like -
Where 1 denotes that the object was selected and 0 denotes that the object was not selected. The environment state being given as input to the DQN consists of attributes for each of the object and other factors of the environment. The DQN would get rewards based on the selection it made. I'm new to reinforcement learning and I've only built DQNs that had to select one action out of the entire action space. But how do I build a DQN or a Reinforcement learning network for this particular environment?