Technical barriers: There should be at least these common sense big barriers:
- Trial-and-error technique makes the model hard to learn (too many), compared to ready-to-use supervised data
- Number of time-steps (which usually equals the number of actions of the agent in the trajectory) is large, thus brute-force exploration won't work as the number of trials to find errors is exponential, although negative rewards may help cut short the brute-force tree.
- Real-life RL takes unlimited number of episodes (for each episode, a sequence of actions should be learnt), and the incremental training is harder and harder in time with more explored data, unless some past and no-longer-related data are removed, just like humans, we forget some of the past to learn more, remember more the present.
The technical barriers are at first the barriers to applying them to business. People may produce some supervised data manually rather quick, and thus supervised learning is usually opted first, nobody wish to try RL.
Harder to find human resources: AI engineers with experiences in supervised learning are more popular and easier to find some; fewer work with RL, thus business projects are not carried out easily if using RL.
However, from my point of view, RL is very much promising in future as AI entities are now more and more on their own.