Inverse Reinforcement Learning Tutorial part I

In this blog post series we will take a closer look at inverse reinforcement learning (IRL) which is the field of learning an agent's objectives, values, or rewards by observing its behavior. For example, we might observe the behavior of a human in some specific task and learn which states of the environment the human is trying to achieve and what the concrete goals might be. This is the first part of this series in which we will get an overview of IRL and look at three basic algorithms to solve the IRL problem. In later parts we will explore more advanced techniques and state of the art methods See section IRL Algorithms . To follow this tutorial, basic knowledge in reinforcement learning (RL) is required.