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SOFTWARE (NON-INTERNET/MOBILE) | Education & Training Software

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About Kogun

The company provides a Graphical Simulation Scenario Generator (GSSG) that allows the design of mission exercise scenarios for air defense and air traffic control applications, focusing on simulation of force elements through both radar and tactical data link exchange. The company also offers a tactical data link upgrade to be configured with the GSSG, which will allow generation of a simulation stream, representing flight paths on different types of tactical data links. The company's products enable the designer to focus only on the mission requirements, and shorten training time for flight simulation.

Kogun Headquarter Location

Lynghals 9

Reykjavik, 110,


354 580 9200

Latest Kogun News

IJCAI 2020丨近期必读七篇【深度强化学习】论文

Sep 29, 2020

IJCAI 2020丨近期必读七篇【深度强化学习】论文 人工智能 根据AMiner-IJCAI 2020词云图,小脉发现     作者:Tianpei Yang、Jianye Hao、Zhaopeng Meng、Zongzhang Zhang、Yujing Hu、Yingfeng Chen、Changjie Fan、Weixun Wang、Wulong Liu、Zhaodong Wang、Jiajie Peng   简介: · The authors propose a Policy Transfer Framework (PTF) which can efficiently select the optimal source policy and exploit the useful information to facilitate the target task learning. · PTF efficiently avoids negative transfer through terminating the exploitation of current source policy and selects another one adaptively. · PTF can be combined with existing deep DRL methods. · Experimental results show PTF efficiently accelerates the learning process of existing state-ofthe-art DRL methods and outperforms previous policy reuse approaches. 2. 论文名称:KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge     简介: · The authors propose a novel policy network framework called KoGuN to leverage human knowledge to accelerate the learning process of RL agents. · The authors firstly evaluate the algorithm on four tasks in Section 4.1 : CartP ole [Barto and Sutton, 1982], LunarLander and LunarLanderContinuous in OpenAI Gym [Brockman et al, 2016] and F lappyBird in PLE [Tasfi, 2016]. · The authors show the effectiveness and robustness of KoGuN in sparse reward setting in Section 4.2. · For PPO without KoGuN, the authors use a neural network with two full-connected hidden layers as policy approximator. · For KoGuN with normal network (KoGuN-concat) as refine module, the authors use a neural network with two full-connected hidden layers for the refine module. · For KoGuN with hypernetworks (KoGuN-hyper), the authors use hypernetworks to generate a refine module with one hidden layer. · All hidden layers described above have 32 units. w1 is set to 0.7 at beginning and decays to 0.1 in the end of training phase 3. 论文名称:Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning     简介: · Deep reinforcement learning (DRL) has wide applications in various challenging fields, such as real-world visual navigation [Zhu et al, 2017], playing games [Silver et al, 2016] and robotic controls [Schulman et al, 2015] · In this work , the authors propose to learn independent skills for efficient skill transfer, where the learned primitive skills with strong correlation are decomposed into independent skills · We take the eigenvalues in Figure 1 as an example: for the case of 6 primitive skills, |Z| = 3 is reasonable since more than 98% component of primitive actions can be represented by three independent components · Effective observation collection and independent skills guarantee the success of low-dimension skill transfer

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