Simple Reinforcement Learning with Tensorflow: Part 2 - Policy-based Agents

After a weeklong break, I am back again with part 2 of my Reinforcement Learning tutorial series. In Part 1, I had shown how to put together a basic agent that learns to choose the more rewarding of two possible options. In this post, I am going to describe how we get from that simple agent to one that is capable of taking in an observation of the world, and taking actions which provide the optimal reward not just in the present, but over the long run. With these additions, we will have a full reinforcement agent. Environments which pose the full problem to an agent are referred to as Markov Decision Processes (MDPs).