Power Allocation in Cognitive Radio with Distributed Antenna System

Martyna, Jerzy

doi:10.1007/978-3-319-67380-6_70

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lagrange multiplier method is to find optimal solution in the case of equality constraint. Optimization problem with inequality constraint can be solved using Lagrange multiplier method and Karush Kuhn-Tucker conditions (KKT) [43]. KKT is a sufficient and necessary condition only when the model is convex, otherwise it is only the necessary condition when used to decide whether the solution obtained by Lagrange multiplier method is optimal [45].…”

Section: Lagrange Multiplier Methodsmentioning

confidence: 99%

Multi-agent cooperation Q-learning algorithm based on constrained Markov Game

Zhu

Huang

et al. 2020

COMSIS J

View full text Add to dashboard Cite

Multi-Agent system has broad application in real world, whose security performance, however, is barely considered. Reinforcement learning is one of the most important methods to resolve Multi-Agent problems. At present, certain progress has been made in applying Multi-Agent reinforcement learning to robot system, man-machine match, and automatic, etc. However, in the above area, an agent may fall into unsafe states where the agent may find it difficult to bypass obstacles, to receive information from other agents and so on. Ensuring the safety of Multi-Agent system is of great importance in the above areas where an agent may fall into dangerous states that are irreversible, causing great damage. To solve the safety problem, in this paper we introduce a Multi-Agent Cooperation Q-Learning Algorithm based on Constrained Markov Game. In this method, safety constraints are added to the set of actions, and each agent, when interacting with the environment to search for optimal values, should be restricted by the safety rules, so as to obtain an optimal policy that satisfies the security requirements. Since traditional Multi-Agent reinforcement learning algorithm is no more suitable for the proposed model in this paper, a new solution is introduced for calculating the global optimum state-action function that satisfies the safety constraints. We take advantage of the Lagrange multiplier method to determine the optimal action that can be performed in the current state based on the premise of linearizing constraint functions, under conditions that the state-action function and the constraint function are both differentiable, which not only improves the efficiency and accuracy of the algorithm, but also guarantees to obtain the global optimal solution. The experiments verify the effectiveness of the algorithm.

show abstract

Section: Lagrange Multiplier Methodsmentioning

confidence: 99%

Multi-agent cooperation Q-learning algorithm based on constrained Markov Game

Zhu

Huang

et al. 2020

COMSIS J

View full text Add to dashboard Cite

show abstract

“…Optimization problem with equality constraints can be solved by using Lagrange multiplier and the one with inequality constraints can be solved by exploiting Lagrange multiplier and Karush-Kuhn-Tucker (KTT) conditions which are necessary and sufficient condition when the model is convex and determine whether the solution obtained by Lagrange multiplier method is optimal [27]. The general form of constrained optimization model is represented by Eq.8, the objective function and the constraint function are differentiable in Eq.8 [28].…”

Section: Lagrange Multipliermentioning

confidence: 99%

Safe Q-Learning Method Based on Constrained Markov Decision Processes

Zhu

Ling

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The application of reinforcement learning in industrial fields makes the safety problem of the agent a research hotspot. Traditional methods mainly alter the objective function and the exploration process of the agent to address the safety problem. Those methods, however, can hardly prevent the agent from falling into dangerous states because most of the methods ignore the damage caused by unsafe states. As a result, most solutions are not satisfactory. In order to solve the aforementioned problem, we come forward with a safe Q-learning method that is based on constrained Markov decision processes, adding safety constraints as prerequisites to the model, which improves standard Q-learning algorithm so that the proposed algorithm seeks for the optimal solution ensuring that the safety premise is satisfied. During the process of finding the solution in form of the optimal state-action value, the feasible space of the agent is limited to the safe space that guarantees the safety via the feasible space being filtered by constraints added to the action space. Because the traditional solution methods are not applicable to the safe Q-learning model as they tend to obtain local optimal solution, we take advantage of the Lagrange multiplier method to solve the optimal action that can be performed in the current state based on the premise of linearizing constraint functions, which not only improves the efficiency and accuracy of the algorithm, but also guarantees to obtain the global optimal solution. The experiments verify the effectiveness of the algorithm. INDEX TERMS Constrained Markov decision processes, safe reinforcement learning, Q-learning, constraint, Lagrange multiplier.

show abstract

Power Allocation in Cognitive Radio with Distributed Antenna System

Cited by 2 publications

References 18 publications

Multi-agent cooperation Q-learning algorithm based on constrained Markov Game

Multi-agent cooperation Q-learning algorithm based on constrained Markov Game

Safe Q-Learning Method Based on Constrained Markov Decision Processes

Contact Info

Product

Resources

About