Reinforcement learning in the real world

Summary form only given. Reinforcement learning refers to improving performance through trial-and-error experience. Although, modern computational approaches to reinforcement learning were inspired by animal learning, they have now branched out in a very several different directions. Some researchers are interested in finding high-quality approximate solutions to large-scale stochastic planning problems that are important for industry and government. Others are pursuing the goal of building intelligent, resourceful autonomous agents that, like animals, can succeed while acting in real-time in complex environments. While these goals have much in common - and they both involve the real world - they represent two very different perspectives on reinforcement learning and related methods. After first reviewing the major elements of modern reinforcement learning and its relationship to optimal control, other types of machine learning, and to neuroscience, I present several striking example applications and describe some of the latest research directed toward scaling up to ever more complex problems. I conclude by laying out a view of the future of reinforcement learning research, emphasizing that the issues that need to be addressed depend strongly on which type of reinforcement learning one has in mind.