In case you didn’t know, I write a monthly column for MSDN Magazine on AI called “Artificially Intelligent”

In the last two articles, I covered one of the most exciting topics in AI in these days: reinforcement learning

Here’s a snippet and link to the full articles on MSDN.

Introduction to Reinforcement Learning

In previous articles, I’ve mentioned both supervised learning and unsupervised learning algorithms. Beyond these two methods of machine learning lays another type: Reinforcement Learning (RL). Formally defined, RL is a computational approach to goal-oriented learning through interaction with the environment under ideal learning conditions.

Like other aspects of AI, many of the algorithms and approaches actively used today trace their origins back to the 1980s (bit.ly/2NZP177). With the advent of inexpensive storage and on-demand compute power, reinforcement learning techniques have re-emerged.

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A Closer Look at Reinforcement Learning

In last month’s column, I explored a few basic concepts of reinforcement learning (RL), trying both a strictly random approach to navigating a simple environment and then implementing a Q-Table to remember both past actions and which actions led to which rewards. In the demo, an agent working randomly was able to reach the goal state approximately 1 percent of the time and roughly half the time when using a Q-Table to remember previous actions. However, this experiment only scratched the surface of the promising and expanding field of RL.

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