Exploration and evaluation of reinforcement learning in production

The typical assertion is that RL does not work in production systems due to the exploratory nature of agents, but is it possible to mitigate some of these assumptions? This talk will be about issues with exploration and evaluation in RL production systems, but also about mitigation in terms of sample-efficiency (for ex. through transfer or distributed/federated learning), safe exploration, and off-policy evaluation.