Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos
An RL Agent needs to interact with the environment to learn and train. Training RL Agents for real-world applications usually comes with physical limitations and challenges. This is because the Agent could potentially cause damage to the real-world system it is dealing with while learning. Fortunately, there are a lot of tasks in the real world that do not necessarily have such challenges, and yet can be very useful for completing the day-to-day real-world tasks that are available in our To-Do lists!
The recipes in this chapter will help you build RL Agents that can complete tasks on the internet, ranging from responding to annoying popups, booking flights on the web, managing emails and social media accounts, and more. We can do all of this without using a bunch of APIs that change over time or utilizing hardcoded scripts that stop working when a web page is updated...