I'm not an expert in AI or NN, I gathered most of the information I have from the internet, and I'm looking for advice and guidance.
I'm trying to design a NN that is going to be used by all the agents of my simulation (each agent will have its own matrix of weights). This is what I plan to have:
- The NN will have 1 input layer and 1 output layer (no hidden layers).
- The number of inputs will always be greater than the number of outputs.
- The outputs represent the probability of an action being taken by the agent (the output node with the highest value will identify the action that will be taken). Which means there are as many output nodes are there are actions.
When an agent takes an action it receives a reward: a number that represents how well the agent performed. This happens "online" that is, the agent is trained on the fly.
What I would like to know if how to best train the NN: that is, how to update the weights of my matrix to maximise the rewards long term.
From the research I made it seems this is close to the concept of Reinforcement Learning, but even if it was, it's not clear to me how to apply it to such a simple NN shape.