Macro action selection with deep reinforcement learning in StarCraft

StarCraft (SC) is one of the most popular and successful Real Time Strategy (RTS) games. In recent years, SC is also widely accepted as a challenging testbed for AI research because of its enormous state space, partially observed information, multi-agent collaboration, and so on. With the help of annual AIIDE and CIG competitions, a growing number of SC bots are proposed and continuously improved. However, a large gap remains between the top-level bot and the professional human player. One vital reason is that current SC bots mainly rely on predefined rules to select macro actions during their games. These rules are not scalable and efficient enough to cope with the enormous yet partially observed state space in the game. In this paper, we propose a deep reinforcement learning (DRL) framework to improve the selection of macro actions. Our framework is based on the combination of the Ape-X DQN and the Long-Short-Term-Memory (LSTM). We use this framework to build our bot, named as LastOrder. Our evaluation, based on training against all bots from the AIIDE 2017 StarCraft AI competition set, shows that LastOrder achieves an 83% winning rate, outperforming 26 bots in total 28 entrants

Использование интернет-технологий для активизации процесса развития въездного туризма в РФ (на примере Томской области)

РЕФЕРАТ Объем работы – 102., рисунков – 7 , таблиц – 2, источников –61 . ИСПОЛЬЗОВАНИЕ ИНТЕРНЕТ ТЕХНОЛОГИЙ ДЛЯ АКТИВИЗАЦИИ ПРОЦЕССА РАЗВИТИЯ ВЪЕЗДНОГО ТУРИЗМА В РФ (НА ПРИМЕРЕ ТОМСКОЙ ОБЛАСТИ) Ключевые слова: интернет – технологии, туризм, въездной туризм, порталы. Актуальность Предмет исследования – исследования является интернет-технологии для повышения привлекательности региона для туристов. Объект исследования – интернет технологии. Проблему исследования Цель данной работы – исследования заключается в разработке предложений по совершенствованию туристского портала томской области. Для достижения поставленной цели определены следующие задачи: • Дать понятие и рассмотреть интернет технологии; • Рассмотреть состояние въездного туризма в зарубежных странах; • ПESSAY Volume of work - 102, figures - 7, tables - 2, sources -61. USING INTERNET TECHNOLOGY TO ENHANCE THE PROCESS OF DEVELOPMENT OF TOURISM IN THE RUSSIAN FEDERATION (THE EXAMPLE OF THE TOMSK REGION) Keywords: Internet - technology, tourism, inbound tourism portals. Relevance Subject of research - the study is an Internet-based technologies to enhance the attractiveness of the region for tourists. The object of study - Internet technology. research problem The purpose of this work - the research is to develop proposals for improving the tourist portal of the Tomsk region. To achieve this goal the following tasks: • Writing the concept and consider the Internet technology; • Consider the state of tourism in foreign countries; • Analyze the impact of Internet technology on the inbound t

Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets

Natural language understanding typically maps single utterances to a dual level semantic frame, sentence level intent and slot labels at the word level. The best performing models force explicit interaction between intent detection and slot filling. We present a novel tri-level joint natural language understanding approach, adding domain, and explicitly exchange semantic information between all levels. This approach enables the use of multi-turn datasets which are a more natural conversational environment than single utterance. We evaluate our model on two multi-turn datasets for which we are the first to conduct joint slot-filling and intent detection. Our model outperforms state-of-the-art joint models in slot filling and intent detection on multi-turn data sets. We provide an analysis of explicit interaction locations between the layers. We conclude that including domain information improves model performance.Comment: accepted at INTERSPEECH 202

Macro action selection with deep reinforcement learning in StarCraft

StarCraft (SC) is one of the most popular and successful Real Time Strategy (RTS) games. In recent years, SC is also considered as a testbed for AI research, due to its enormous state space, hidden information, multi-agent collaboration and so on. Thanks to the annual AIIDE and CIG competitions, a growing number of bots are proposed and being continuously improved. However, a big gap still remains between the top bot and the professional human players. One vital reason is that current bots mainly rely on predefined rules to perform macro actions. These rules are not scalable and efficient enough to cope with the large but partially observed macro state space in SC. In this paper, we propose a DRL based framework to do macro action selection. Our framework combines the reinforcement learning approach Ape-X DQN with Long-Short-Term-Memory (LSTM) to improve the macro action selection in bot. We evaluate our bot, named as LastOrder, on the AIIDE 2017 StarCraft AI competition bots set. Our bot achieves overall 83% win-rate, outperforming 26 bots in total 28 entrants

Acoustic emission source location method and experimental verification for structures containing unknown empty areas

Acoustic emission (AE) localization plays an important role in the prediction and control of potential hazardous sources in complex structures. However, existing location methods have less discussion on the presence of unknown empty areas. This paper proposes an AE source location method for structures containing unknown empty areas (SUEA). Firstly, this method identifies the shape, size, and location of empty areas in the unknown region by exciting the active AE sources and using the collected AE arrivals. Then, the unknown AE source can be located considering the identified empty areas. The lead break experiments were performed to verify the effectiveness and accuracy of the proposed method. Five specimens were selected containing empty areas with different positions, shapes, and sizes. Results show the average location accuracy of the SUEA increased by 78% compared to the results of the existing method. It can provide a more accurate solution for locating AE sources in complex structures containing unknown empty areas such as tunnels, bridges, railroads, and caves in practical engineering