This is an Inverse Reinforcement Learning (IRL) problem. I have data (observations) on actions taken by a (real) agent. Given this data I want to estimate the likelihood of the observed actions in a Q-learning agent. Rewards are given by a linear function on a parameter, say alpha.
Thus, I want to estimate the alpha that makes the observed actions more likely to be taken by a Q-agent. I read some papers (i.e. Ng & Russel 2004), but I found them rather generalistic.