I wonder if it’s possible to incorporate a monkey see monkey do aspect to the learning algorithm that could observe human’s playing the game and incorporate that information into its models?
Yes, it's called imitation learning and is a subfield of reinforcement learning. The problem is that even a small error could gradually accumulate and cause the sequence of actions to diverge. RL agents learn not just how to act in a given situation but also to evaluate possible actions, situations and even to model the environment. That way they can adapt dynamically instead of diverging from the optimal actions.
Interesting, ideally it uses the observed human behaviors to seed/inform it’s own attempts as a shortcut to advanced behavior without the many millions of generations needed.
