Substation Operation Sequence Inference Model Based on Deep Reinforcement Learning
نویسندگان
چکیده
At present, substation operation ticket system is developed based on an expert system, which has some problems such as knowledge base redundancy, intelligence deficiency and automatic learning ability. To solve this problem, paper proposes sequence reasoning model the of Neo4j graph DuelingDQN (Dueling Deep Q Network) algorithm. Firstly, diagram structure main wiring was established using graph. Based model, operable equipment set task searched to form space, action space selection DuelingDQN. The reward punishment function designed “five defense” rules state change equipment. Make interact in real time, automatically learn sequence. test results show that method proposed can deduce correct steps under different modes realize transfer within station, great significance intellectualization system.
منابع مشابه
Operation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm
: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...
متن کاملMulti-task learning with deep model based reinforcement learning
In recent years, model-free methods that use deep learning have achieved great success in many different reinforcement learning environments. Most successful approaches focus on solving a single task, while multi-task reinforcement learning remains an open problem. In this paper, we present a model based approach to deep reinforcement learning which we use to solve different tasks simultaneousl...
متن کاملVision-based Deep Reinforcement Learning
Recently, Google Deepmind showcased how Deep learning can be used in conjunction with existing Reinforcement Learning (RL) techniques to play Atari games[11], beat a world-class player [14] in the game of Go and solve complicated riddles [3]. Deep learning has been shown to be successful in extracting useful, nonlinear features from high-dimensional media such as images, text, video and audio [...
متن کاملmortality forecasting based on lee-carter model
over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...
15 صفحه اولEfficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
Modern reinforcement learning algorithms reach super–human performance on many board and video games, but they are sample inefficient, i.e. they typically require significantly more playing experience than humans to reach an equal performance level. To improve sample efficiency, an agent may build a model of the environment and use planning methods to update its policy. In this article we intro...
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: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13137360