Journal Article10.1016/j.entcom.2024.100670
Deep reinforcement learning algorithm based on multi-agent parallelism and its application in game environment
Chao Liu,Di Liu +1 more
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About: This article is published in Entertainment Computing. The article was published on 01 Apr 2024.
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Citations
Water environment governance under the Central Environmental Protection Inspection mechanism: a collaborative governance strategy from a multi-agent perspective
Yanan Zhao,Lili Zhang,Siyao Li +2 more
TL;DR: This study analyzes China's Central Environmental Protection Inspection through a tripartite evolutionary game model, revealing how financial incentives, oversight, and inspections align decentralized actors' behaviors, achieving compliance without performance incentives, and guiding incentive calibration in environmental governance.
References
Deep Reinforcement Learning for Autonomous Driving: A Survey
B Ravi Kiran,Ibrahim Sobh,Victor Talpaert,Patrick Mannion,Ahmad A. Al Sallab,Senthil Yogamani,Patrick Pérez +6 more
TL;DR: This review summarises deep reinforcement learning algorithms, provides a taxonomy of automated driving tasks where (D)RL methods have been employed, highlights the key challenges algorithmically as well as in terms of deployment of real world autonomous driving agents, the role of simulators in training agents, and finally methods to evaluate, test and robustifying existing solutions in RL and imitation learning.
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Deep Reinforcement Learning for Autonomous Driving: A Survey
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Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning
TL;DR: This paper develops a DRL based algorithm, in which the joint design is obtained through trial-and-error interactions with the environment by observing predefined rewards, in the context of continuous state and action, and obtains the comparable performance compared with two state-of-the-art benchmarks.
Research Design and Methods: A Systematic Review of Research Paradigms, Sampling Issues and Instruments Development
TL;DR: In this paper, a detailed systematic review on research paradigms, sampling and instrument development issues in the field of business research is presented, where the main contribution of this study was to explore the sampling size issues.
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