[A version of this post appears on the O’Reilly Radar.]
An overview of commercial and industrial applications of reinforcement learning.
The flurry of headlines surrounding AlphaGo Zero (the most recent version of DeepMind’s AI system for playing Go) means interest in reinforcement learning (RL) is bound to increase. Next to deep learning, RL is among the most followed topics in AI. For most companies, RL is something to investigate and evaluate but few organizations have identified use cases where RL may play a role. As we enter 2018, I want to briefly describe areas where RL has been applied.
RL is confusingly used to refer to a set of problems and a set of techniques, so let’s first settle on what RL will mean for the rest of this post. Generally speaking, the goal in RL is learning how to map observations and measurements to a set of actions while trying to maximize some long-term reward. This usually involves applications where an agent interacts with an environment while trying to learn optimal sequences of decisions. In fact, many of the initial applications of RL are in areas where automating sequential decision-making have long been sought. RL poses a different set of challenges from traditional online learning, in that you often have some combination of delayed feedback, sparse rewards, and (most importantly) the agents in question are often able to affect the environments with which they interact.
Continue reading “Practical applications of reinforcement learning in industry”