论文标题
最大因果熵的底漆逆增强学习
A Primer on Maximum Causal Entropy Inverse Reinforcement Learning
论文作者
论文摘要
逆增强学习（IRL）算法推断出一种奖励功能，该奖励功能解释了在环境中行动的专家提供的证明。最大因果熵（MCE）IRL目前是IRL最受欢迎的配方，并具有许多扩展。在本教程中，我们提出了MCE IRL的压缩推导，以及当代IRL算法实施的关键结果。我们希望这既可以作为新领域的人的入门资源，又是对这些主题熟悉的人的简洁参考。
Inverse Reinforcement Learning (IRL) algorithms infer a reward function that explains demonstrations provided by an expert acting in the environment. Maximum Causal Entropy (MCE) IRL is currently the most popular formulation of IRL, with numerous extensions. In this tutorial, we present a compressed derivation of MCE IRL and the key results from contemporary implementations of MCE IRL algorithms. We hope this will serve both as an introductory resource for those new to the field, and as a concise reference for those already familiar with these topics.
