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Deep Q-Network with Predictive State Models in Partially Observable Domains
المؤلفون المشاركون
Yu, Danning
Ni, Kun
Liu, Yunlong
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-16
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
While deep reinforcement learning (DRL) has achieved great success in some large domains, most of the related algorithms assume that the state of the underlying system is fully observable.
However, many real-world problems are actually partially observable.
For systems with continuous observation, most of the related algorithms, e.g., the deep Q-network (DQN) and deep recurrent Q-network (DRQN), use history observations to represent states; however, they often make computation-expensive and ignore the information of actions.
Predictive state representations (PSRs) can offer a powerful framework for modelling partially observable dynamical systems with discrete or continuous state space, which represents the latent state using completely observable actions and observations.
In this paper, we present a PSR model-based DQN approach which combines the strengths of the PSR model and DQN planning.
We use a recurrent network to establish the recurrent PSR model, which can fully learn dynamics of the partially continuous observable environment.
Then, the model is used for the state representation and update of DQN, which makes DQN no longer rely on a fixed number of history observations or recurrent neural network (RNN) to represent states in the case of partially observable environments.
The strong performance of the proposed approach is demonstrated on a set of robotic control tasks from OpenAI Gym by comparing with the technique with the memory-based DRQN and the state-of-the-art recurrent predictive state policy (RPSP) networks.
Source code is available at https://github.com/RPSR-DQN/paper-code.git.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yu, Danning& Ni, Kun& Liu, Yunlong. 2020. Deep Q-Network with Predictive State Models in Partially Observable Domains. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1193352
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yu, Danning…[et al.]. Deep Q-Network with Predictive State Models in Partially Observable Domains. Mathematical Problems in Engineering No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1193352
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yu, Danning& Ni, Kun& Liu, Yunlong. Deep Q-Network with Predictive State Models in Partially Observable Domains. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1193352
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1193352
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