A Deep-Q Network (DQN) is a reinforcement learning technique that attempts to model the actions that perform best in each state in real-time.
In this article, we summarize our SAS research paper on the application of reinforcement learning to monitor traffic control signals which was recently accepted to the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. This annual conference is hosted by the Neural Information Processing Systems Foundation, a non-profit corporation that promotes the exchange of ideas in neural information processing systems across multiple disciplines.
My first post on customer journey optimization set the scene by introducing the concept of marketing as a game to be won. The rules to this game are complex, some are known, and others can be learned. Above all, the game is built on a shifting landscape of customer, competitor
In my previous post I wrote about the Atari video game, Breakout, and how an AI technique (reinforcement learning, or RL) outperformed a human player. I also drew an analogy between Breakout and customer journey optimization. In Breakout, the environment is what you see on the screen – the blocks,
The customer journey is at the forefront of every discussion about modern marketing. The idea that customers move in premeditated or, at the very least, marketer-meditated paths between well-defined states is alluring (and comforting) to a marketing professional. Of course, even a cursory examination of a web path analytics (Sankey)
