Potentials of reinforcement learning in contemporary scenarios

This paper reviews the present applications of reinforcement learning in five major spheres including mobile autonomy, industrial finance and trading, gaming. The application real time cannot be overstated, it encompasses areas far beyond scope this paper, but not limited to medicine, health care, natural language processing, robotics e-commerce. Contemporary research teams have made remarkable progress games comparatively less medical field. Most recent implementations are focused on model-free algorithms as they relatively easier implement. seeks model-based notions, articulate how can efficient contemporary scenarios. Model based is a fundamental approach sequential decision making, refers optimal behavior indirectly by model environment, from taking actions observing outcomes that include subsequent sate instant reward. Many other connection learning. findings could both academic ramifications, enabling individual.