Creating new air combat tactics and discovering novel maneuvers can require
numerous hours of expert pilots' time. Additionally, for each different combat
scenario, the same strategies may not work since small changes in equipment
performance may drastically change the air combat outcome. For this reason, we
created a reinforcement learning environment to help investigate potential air
combat tactics in the field of beyond-visual-range (BVR) air combat: the BVR
Gym. This type of air combat is important since long-range missiles are often
the first weapon to be used in aerial combat. Some existing environments
provide high-fidelity simulations but are either not open source or are not
adapted to the BVR air combat domain. Other environments are open source but
use less accurate simulation models. Our work provides a high-fidelity
environment based on the open-source flight dynamics simulator JSBSim and is
adapted to the BVR air combat domain. This article describes the building
blocks of the environment and some use cases.

使用强化学习环境研究超视距空战领域的潜在空战策略，提供基于开源飞行动力学模拟器 JSBSim 的高保真度环境，描述了环境的构建组成和一些应用案例。

BVR Gym: 超视距空战的增强学习环境

BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range  Air Combat

We apply recurrent neural networks to the task of recognizing surgical
activities from robot kinematics. Prior work in this area focuses on
recognizing short, low-level activities, or gestures, and has been based on
variants of hidden Markov models and conditional random fields. In contrast, we
work on recognizing both gestures and longer, higher-level activites, or
maneuvers, and we model the mapping from kinematics to gestures/maneuvers with
recurrent neural networks. To our knowledge, we are the first to apply
recurrent neural networks to this task. Using a single model and a single set
of hyperparameters, we match state-of-the-art performance for gesture
recognition and advance state-of-the-art performance for maneuver recognition,
in terms of both accuracy and edit distance. Code is available at
this https URL .

使用循环神经网络识别机器人运动学中的外科手术活动，成果表明使用 RNN 可以有效识别手势和操作，并提高了准确性和编辑距离。