Training reinforcement learning agents that continually learn across multiple environments is a challenging problem. This is made more difficult by a lack of reproducible experiments and standard metrics for comparing different continual learning approaches. Researchers can define and share their own curricula over various learning environments or run against a curriculum created under the DARPA Lifelong Learning Machines (L2M) Program. In the last decade, reinforcement learning (RL) with deep neural networks has been successfully applied in a wide variety of domains (Arulkumaran et al., 2017). In typical RL scenarios, the RL agent learns a single task, defined as a single Partially Observable Markov Decision Process (POMDP).

As 2022 begins, the progress and continuing evolution of AI is in full swing across the worlds of industry, manufacturing, retail, banking and finance, healthcare and medicine and an expanding range of other fields. In our first roundup of 2022 enterprise AI predictions from IT leaders, which was published Jan. 7, we received a wide range of predictions from industry experts who shared their thoughts with EnterpriseAI. Today, in this article, we are publishing a second installment of 2022 predictions, which again have been edited for clarity and brevity, to give our readers additional insights into what may come in 2022 in enterprise AI and related technologies. Vijay Tella, the CEO and co-founder at integration platform vendor Workato, said he sees 2022 being the year when the role of enterprise automation architects become one of the most important hires inside companies. "An enterprise automation architect is an emerging role that will surely make waves in 2022," said Tella.