What are the biggest barriers to get RL in production? I am studying the state of the art of Reinforcement Learning, and my point is that we see so many applications in the real world using Supervised and Unsupervised learning algorithms in production, but I don't see the same thing with Reinforcement Learning algorithms.
What are the biggest barriers to get RL in production?
There is a relatively recent paper that tackles this issue: Challenges of real-world reinforcement learning (2019) by Gabriel Dulac-Arnold et al., which presents all the challenges that need to be addressed to productionize RL to real world problems, the current approaches/solutions to solve the challenges, and metrics to evaluate them. I will only list them (based on the notes I had taken a few weeks ago). You should read the paper for more details. In any case, for people that are familiar with RL, they will be quite obvious.

Batch off-line and off-policy training

One current solution is importance sampling


Learning on the real system from limited samples (sample inefficiency)

Solutions: MAML, use expert demonstrations to bootstrap the agent, model-based approaches


High dimensional continuous state and action spaces

Solutions: AE-DQN, DRRN


Satisfying safety constraints

Solutions: constrained MDP, safe exploration strategies, etc.


Partial observability and non-stationarity

Solutions to partial observability: incorporate history in the observation, recurrent neural networks, etc.
Solutions to non-stationarity: domain randomization or system identification


Unspecified and multi-objective reward functions

Solutions: CVaR, Distributional DQN


Explainability
Real-time inference
System delays (see also this and this answers)

There's also a more recent and related paper An empirical investigation of the challenges of real-world reinforcement learning (2020) by Gabriel Dulac-Arnold et al, and here you have the associated code with the experiments.
However, note that RL (in particular, bandits) is already being used to solve at least one real-world problem [1, 2]. See also this answer.