Reinforcement Learning With Variable Actions

What you describe is nothing unusual. Reinforcement learning is a way of finding the value function of a Markov Decision Process. In an MDP, every state has its own set of actions. To proceed with reinforcement learning application, you have to clearly define what the states, actions, and rewards are in your problem.

If you have a number of actions for each soldier that are available or not depending on some conditions, then you can still model this as selection from a fixed set of actions. For example:

• Create a "utility value" for each of the full set of actions for each soldier
• Choose the highest valued action, ignoring those actions that are not available at a given time

If you have multiple possible targets, then the same principle applies, except this time you model your utility function to take the target designation as an additional parameter, and run the evaluation function multiple times (one for each target). You pick the target that has the highest "attack utility".