Reinforcement Learning: How to use neural network toolbox for action value approximation?

I need to use action value approximation Q-learning with a neural network. I have a little knowledge of Reinforcement Learning (RL) but I have never used neural networks and I would like to use only built-in MATLAB commands. From literature research, I understand that I have to update the network weights incrementally each time I take an action, reach a state and get a reward. My state is a two dimensional vector with entries characterizing 15 possible degradation values. I have 25 possible actions, but some of them cannot be performed in some states. I think I should use a network with two inputs and 25 outputs. How do I initialize the network weights properly? And then, how can I update the weight values during RL simulation? I have used adapt and train with 'trainr' option with no success. Thank you

[Greg Heath]

What does RL mean?

Greg

[luca bellani]

Thanks for your answer. RL stands for Reinforcement Learning.