If you have recurring ear, nose, or throat problems despite treatment from your primary care provider, it may be time to see an otolaryngologist—also known as an ear, nose, and throat (ENT) doctor. University of Iowa Health Care’s otolaryngologists care for adults and children.

Located right off of Interstate Highway 80 in Coralville, our Iowa River Landing location offers primary and specialty care, in one convenient location. (For your safety, to limit the spread of COVID-19, all outdoor playgrounds are closed until further notice.) Natural lighting is used to illuminate hallways, offices, and reception areas.

This is an outpatient facility of University of Iowa Health Care Medical Center.

首先从约束优化的角度考虑这个问题，相比 MaxEnt IRL，这里的优化目标是最大化因果熵： 这个优化并不一定的是凸的，但是我们也用 凸优化 的标准方法处理：（1）写拉格朗日（2）写 对偶问题 （3）对偶上升。

Maximum Entropy Inverse Reinforcement Learning 对算法梗概进行了介绍，给出了另一种推导过程，可以对照学习。 逆强化学习 (IRL)要解决的关键问题是：根据专家示教数据倒推 回报函数。 Notation: \displaystyle {\tau = \ {s_1,a_1,...,s_t,a_t,...,s_T\}} R_ {\psi} (\tau) = \sum_ {t} r_\psi (s_t,a_t) ( \psi are learned parameters.) \displaystyle {\ {\tau_i\}\sim \pi^*} MaxEnt formulation:

Maximum Entropy Inverse Reinforcement Learning - notes and tutorial for IRL using the principle of maximum entropy Topics

比较出名的工作就是最大熵IRL（MaxEnt IRL）。 这个工作最早是 Ziebart 在2008年时候的工作《Maximum Entropy Inverse Reinforcement Learning》，提出用最大熵的原则来解决这个ambiguous的问题。

We take pride in providing leading-edge patient care, advancing the field of otolaryngology through first-rate research, and offering superior education to the next generation of high-quality head and neck specialists.

If you need treatment for an ear, nose, or throat (ENT) problem, University of Iowa Health Care can help. Our experienced team cares for adults and children. It includes otolaryngologists (ENT specialists) who treat a broad range of conditions and subspecialists with advanced training in specific disorders or procedures.

This is a python implementation of the Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) algorithm based on the similarly named paper by Ziebart et al. and the Maximum Causal Entropy Inverse Reinforcement Learning (MaxCausalEnt IRL) algorithm based on …