Inverse reinforcement learning

In Chapter 9, Robot Control System Using Deep Reinforcement Learning, we addressed the amazing world of the reinforcement learning. Reinforcement learning aims to create algorithms that can learn and adapt to environmental changes. This programming technique is based on the concept of receiving external stimuli, the nature of which depends on the algorithm choices. A correct choice will involve a reward, while an incorrect choice will lead to a penalty. The goal of the system is to achieve the best possible rewards, of course. Often, the reward function can be difficult to define: it is not always easy to understand whether a certain action in a certain state is positive for the agent. The purpose of IRL is to identify it. In IRL, the reward function is derived from the observed behavior. As we have learned, in reinforcement learning, we use rewards...