Deep Reinforcement Learning Hands-On

Simple clicking approach

As the first demo, let's implement a simple Asynchronous Advantage Actor-Critic (A3C) agent, which decides where it should click on given the image observation. This approach can solve only a small subset of the full MiniWoB suite and we'll discuss restrictions of this approach later. For now, it will allow us to get a better understanding of the problem.

As with the previous chapter, due to size of the code, I won't put a complete source code here. We'll focus on the most important functions and give the rest as an overview. The complete source code is available in the GitHub repository https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On.

Grid actions

When we talked about OpenAI Universe's architecture ...