Can reinforcement learning solve the NP-Hard problems?

For an algorithm to be termed "efficient", its execution time must be constrained by a polynomial function of the input size. It was realised early on that not all issues could be handled thus rapidly, but it was difficult to determine which ones could and which couldn't. Some so-called NP-hard issues are thought to be impossible to answer in polynomial time. NP-hard stands for non-deterministic polynomial-time hardness. This article will be focused on understanding some NP-hard problems and trying to solve them with Reinforcement Learning. Following are the topics to be covered.