Understanding Inverse RL

In RL algorithms, the agent receives a reinforcement signal as soon as they perform an action. There are several areas where it is difficult to estimate a reinforcement function. Inverse RL (IRL) allows you to reconstruct a reinforcement function that defines the behavior of an agent based on a set of demonstrations. When learning about reverse reinforcement, the reward function derives from the observed behavior. Generally, in RL, we use rewards to learn the behavior of a system. In IRL, this function is reversed; in fact, the agent observes the behavior of the system to understand what the goal is trying to achieve. To do this, it is necessary to have the state of the environment available to learn the optimal policy ...

Get Hands-On Reinforcement Learning with R now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.