Well, basically all the author did was rediscover reinforcement learning, but with a bit of additional heuristics in how to do epsilon-greedy exploration. I bet 50/50 that the algorithm will work with some tuning and training time, but I doubt what the author said can be extended to anything much more complex than those these simple OpenAI Gym experiments.

The problem I have with their argument is a lot more philosophical: it all boils down to the validity of the reward hypothesis (http://incompleteideas.net/rlai.cs.ualberta.ca/RLAI/rewardhy...), and how we even define “intelligent” behavior. I’ve written another comment somewhere in this thread for more detail.