Mastering Machine Learning Algorithms

Rewards

We have seen that rewards (sometimes negative rewards are called penalties, but it's preferable to use a standardized notation) are the only feedback provided by the environment after each action. However, there are two different approaches to the use of rewards. The first one is the strategy of a very short-sighted agent and consists in taking into account only the reward just received. The main problem with this approach is clearly the inability to consider longer sequences that can lead to a very high reward. For example, an agent has to traverse a few states with negative reward (for example, -0.1), but after them, they arrive at a state with a very positive reward (for example, +5.0). A short-sighted agent couldn't  find out ...