Php21 Pharmaceutical Policy in Greece: Recent Developments and the Role of Pharmacoeconomics

Vandoros, Christina; Geitona, M; Kontozamanis, Vassilis; Karokis, A

doi:10.1016/s1098-3015(10)67722-4

Cited by 19 publications

(3 citation statements)

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“…For the fuzzy inference model with n inputs and y membership functions, y n possible q-functions need to be considered simultaneously if the traditional Deep Q Networks algorithm is used directly. This makes the algorithm of Deep Q Networks difficult and even hard to explore effectively in the application of multiple discrete behaviors [21].…”

Section: Sharing Behavior Decision Based On Improved Deep Q Networkmentioning

confidence: 99%

Branching improved Deep Q Networks for solving pursuit-evasion strategy solution of spacecraft

Bingyan¹,

Ye²,

Dong³

et al. 2022

JIMO

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<p style='text-indent:20px;'>With the continuous development of space rendezvous technology, more and more attention has been paid to the study of spacecraft orbital pursuit-evasion differential game. Therefore, we propose a pursuit-evasion game algorithm based on branching improved Deep Q Networks to obtain a space rendezvous strategy with non-cooperative target. Firstly, we transform the optimal control of space rendezvous between spacecraft and non-cooperative target into a survivable differential game problem. Next, in order to solve this game problem, we construct Nash equilibrium strategy and test its existence and uniqueness. Then, in order to avoid the dimensional disaster of Deep Q Networks in the continuous behavior space, we construct a TSK fuzzy inference model to represent the continuous space. Finally, in order to solve the complex and timeconsuming self-learning problem of discrete action sets, we improve Deep Q Networks algorithm, and propose a branching architecture with multiple groups of parallel neural Networks and shared decision modules. The simulation results show that the algorithm achieves the combination of optimal control and game theory, and further improves the learning ability of discrete behaviors. The algorithm has the comparative advantage of continuous space behavior decision, can effectively deal with the continuous space chase game problem, and provides a new idea for the solution of spacecraft orbit pursuit-evasion strategy.</p>

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Section: Sharing Behavior Decision Based On Improved Deep Q Networkmentioning

confidence: 99%

Branching improved Deep Q Networks for solving pursuit-evasion strategy solution of spacecraft

Bingyan¹,

Ye²,

Dong³

et al. 2022

JIMO

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“…MADMI-TD3 is a deep reinforcement learning algorithm developed from DDPG [34][35]. In order to overcome the over-estimation of Q value [36][37] and the low training efficiency problem [38] in DDPG, the algorithm adopted seven techniques for improvement of the stability and training efficiency.…”

Section: B Madmi-td3 Frameworkmentioning

confidence: 99%

Virtual Generation Alliance Automatic Generation Control Based on Deep Reinforcement Learning

2020

IEEE Access

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This paper proposes a distributed hierarchical automatic generation control (AGC) framework with multiple regulation units in the performance-based frequency regulation market, named virtual generation alliance automatic generation control (VGA-AGC), aiming to achieve the coordination of control algorithm and AGC dispatch algorithm and adapt to the development trend of AGC from centralized framework to centralized-decentralized framework. The framework also involves a multi agent distributed multiple improved deep deterministic policy gradient (MADMI-TD3) algorithm that is characterized by excellent global search capability and optimizing speed. The algorithm can help create an optimal AGC strategy in a randomization environment so as to obtain an optimal cooperative control of AGC. According to a simulation verification on the LFC model for an interconnected power grid of a province, the algorithm is superior to the current algorithms and conventional engineering methods in terms of control performance and economic benefits. In other words, the algorithm can improve control performance and reduce the regulation mileage payment. INDEX TERMS performance-based frequency regulation market; virtual generation alliance automatic generation control (VGA-AGC); multi agent distributed multiple improved deep deterministic policy gradient; regulation mileage payment; centralized-decentralized autonomy

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“…For these reasons, the PEMFC requires a model-free algorithm that can perform parameter tracking independent of the PEMFC, which is guided by simple control principles Yang et al, 2021a;Yang et al, 2021b;Yang et al, 2021c). The Deep Deterministic Policy Gradient (DDPG) algorithm in deep reinforcement learning (Lillicrap et al, 2015) is a model-free method (Yang et al, 2018;Yang et al, 2019a;Yang et al, 2019b;. Due to its strong adaptive ability, the DDPG algorithm can adapt to the uncertainty inherent in nonlinear control systems, and it is applied in various control fields (Zhang et al, 2019;Zhao et al, 2020;Zhang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Temperature Control of Proton Exchange Membrane Fuel Cell Based on Machine Learning

2021

Front. Energy Res.

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In order to improve the proton exchange membrane fuel cell (PEMFC) working efficiency, we propose a deep-reinforcement-learning based PID controller for realizing optimal PEMFC stack temperature. For this purpose, we propose the Improved Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, a tuner of the PID controller, which can adjust the coefficients of the controller in real time. This algorithm accelerates the learning speed of an agent by continuously changing the soft update parameters during the training process, thereby improving the training efficiency of the agent, and further reducing training costs and obtaining a robust strategy. The effectiveness of the control algorithm is verified through a simulation in which it is compared against a group of existing algorithms.

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Php21 Pharmaceutical Policy in Greece: Recent Developments and the Role of Pharmacoeconomics

Cited by 19 publications

References 0 publications

Branching improved Deep Q Networks for solving pursuit-evasion strategy solution of spacecraft

Branching improved Deep Q Networks for solving pursuit-evasion strategy solution of spacecraft

Virtual Generation Alliance Automatic Generation Control Based on Deep Reinforcement Learning

Temperature Control of Proton Exchange Membrane Fuel Cell Based on Machine Learning

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