Virtual-Taobao: Virtualizing Real-World Online Retail Environment for Reinforcement Learning
نویسندگان
چکیده
منابع مشابه
Real World Reinforcement Learning
Self-sustenance in an unknown environment for an autonomous mobile robot with restricted sensory faculties is a challenging problem, primarily due to lack of effectiveness of existing techniques (deterministic or learning-based) in real world. Through this thesis, we try to investigate the utility of some of the existing reinforcement learning based techniques in achieving energy self-sufficien...
متن کاملVirtualizing Real-World Objects
High quality, virtual 3D models are quickly emerging as a new multimedia data type with applications in such diverse areas as e-commerce, online encyclopaedias, or virtual museums, to name just a few. This paper presents new algorithms and techniques for the acquisition and real-time interaction with complex textured 3D objects and shows how these results can be seamlessly integrated with previ...
متن کاملA Virtual Learning Environment for Real-World Networking
Virtual learning environments are a solution to some of the problems of providing an authentic learning environment. We encountered problems such as lack of funding and physical space, and risks and threats to our network environment when we contemplated providing a real, physical specialist laboratory to teach computer networking. We solved most of our problems by developing Velnet, a virtual ...
متن کاملVirtualizing Real-World Objects in FRP
We begin with a functional reactive programming (FRP) model in which every program is viewed as a signal function that converts a stream of input values into a stream of output values. We observe that objects in the real world – such as a keyboard or sound card – can be thought of as signal functions as well. This leads us to a radically different approach to I/O – instead of treating real-worl...
متن کاملLearning from Demonstrations for Real World Reinforcement Learning
Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-wo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33014902