The process of discovering and incorporating knowledge from educators into machine learning (ML) poses major challenges. Beyond coding the known or derived characteristics of systems into ML algorithms, how should innovative researchers discover, design, and implement algorithms for diverse, complex artificial intelligence (AI) applications? Adams et al. present a pertinent, comprehensive literature review for understanding inverse reinforcement learning (IRL) in complicated AI applications.
The authors introduce formal Markov decision processes (MDPs) for understanding how sequential decisions work in optimum policy learning algorithms such as inverse optimal control (IOC) and apprenticeship learning algorithms. It is important that readers understand the roles of states, actions, transformations, and rewards in alternative models, as well as the use of MDPs in a variety of IRL applications. The authors’ great efforts help to make the elements of MDPs in complex AI application environments accessible to non-mathematically inclined audiences.
Undeniably, reinforcement learning can be used to ascertain the best possible control policies for AI applications, though at the expense of enormous discovery interactions in intricate environments. However, IRL might be used to learn the constraints and constructs of concise depictions of tasks from expert knowledge, and then applying it to optimal control policies in complex AI applications.
The decision on when to use reinforcement learning or IRL is not straightforward. It is important to understand the variations, similarities, and effectiveness of the current IOC and IRL algorithms. This engaging literature review is recommended for AI practitioners interested in IRL and creating more revolutionary global AI applications.