Codes and demo are available. This article explores what are states, actions and rewards in reinforcement learning, and how agent can learn through simulation to determine the best actions to take in any given state. After a long day at work, you are deciding between 2 choices: to head home and write an article or hang out with friends at a bar. If you choose to hang out with friends, your friends will make you feel happy; whereas heading home to write an article, you'll end up feeling tired after a long day at work. In this example, enjoying yourself is a reward and feeling tired is viewed as a negative reward, so why write articles?
In Airflow, to describe the status of a DAG or a Task that is waiting to execute the next steps, we have defined State to share that information on the progress of the pipeline. Without the State, the execution of any DAG or task becomes a black box, and you might need to create additional external flag or resources to check status to help determine if a job finished or failed. Fortunately, Airflow provides the mechanism of State and stores each of the last recorded states in its backend DB. Not only this way is easy to watch the status of any job in Airflow UI or DB, but it's also a persistent layer to help rerun or backfill while confronting failure. In this article, we are going to discuss the fundamental of what is the Airflow State, what types are those states, how to use the Airflow State to test, and debug.
In larger React apps, it's really nice to have global state (global as in globally accessible, NOT stored as a global browser variable). Alternatively, you can use nested React state to have nicer variable names to deal with. ReduxX is incredibly simple to install and learn. Everything you need to know is contained in this README.md For ReduxX you only need to follow the 3 simple steps in the How ReduxX Works Section below!