Results 21 to 30 of about 6,497 (242)
Indirect Dynamic Negotiation in the Nash Demand Game
IEEE Access, 2022 The paper addresses a problem of sequential bilateral bargaining with incomplete information. We proposed a decision model that helps agents to successfully bargain by performing indirect negotiation and learning the opponent’s model ...
Tatiana V. Guy +2 more
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A Markov chain Monte Carlo algorithm for Bayesian policy search
Systems Science & Control Engineering, 2018 Policy search algorithms have facilitated application of Reinforcement Learning (RL) to dynamic systems, such as control of robots. Many policy search algorithms are based on the policy gradient, and thus may suffer from slow convergence or local optima ...
Vahid Tavakol Aghaei +2 more
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BMC Medical Informatics and Decision Making, 2019 Background Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. To ensure such applications, an explicit reward function encoding domain knowledge should be specified ...
Chao Yu, Jiming Liu, Hongyi Zhao
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A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
Applied Sciences, 2023 Vehicular ad hoc networks (VANETs) are crucial components of intelligent transportation systems (ITS) aimed at enhancing road safety and providing additional services to vehicles and their users.
Xiaofeng Liu +2 more
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Bounds on the bias terms for the Markov reward approach
, 2019 An important step in the Markov reward approach to error bounds on stationary performance measures of Markov chains is to bound the bias terms. Affine functions have been successfully used for these bounds for various models, but there are also models for which it has not been possible to establish such bounds.
Bai, Xinwei, Goseling, Jasper
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Differentially Private Reward Functions in Markov Decision Processes: Policy Synthesis and Tradeoffs
IEEE Open Journal of Control SystemsPolicy synthesis in Markov decision processes uses a known reward function to compute a policy that maximizes it. However, onlookers may infer reward functions by observing agents, which can reveal sensitive information.
Alexander Benvenuti +6 more
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Research on Wargame Decision-Making Method Based on Multi-Agent Deep Deterministic Policy Gradient
Applied Sciences, 2023 Wargames are essential simulators for various war scenarios. However, the increasing pace of warfare has rendered traditional wargame decision-making methods inadequate.
Sheng Yu, Wei Zhu, Yong Wang
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Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model. [PDF]
PLoS Computational Biology, 2015 Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species.
Greg Jensen +4 more
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Ain Shams Engineering Journal, 2023 With the price of green energy now more reasonable, users can now produce enough electricity to meet their needs and make a profit by selling the surplus on the underground P2P energy market.
Mohammed Alsolami +3 more
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Dynamic Mechanism Design for Repeated Markov Games with Hidden Actions: Computational Approach
Mathematical and Computational ApplicationsThis paper introduces a dynamic mechanism design tailored for uncertain environments where incentive schemes are challenged by the inability to observe players’ actions, known as moral hazard.
Julio B. Clempner
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