scholarly journals Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 543
Author(s):  
Soyi Jung ◽  
Won Joon Yun ◽  
Joongheon Kim ◽  
Jae-Hyun Kim
Keyword(s):  
Big Data ◽  
Reinforcement Learning ◽  
Resource Sharing ◽  
Learning Algorithm ◽  
Energy Resource ◽  
Energy Aware ◽  
Aerial Surveillance ◽  
Performance Improvements ◽  
Multi Agent ◽  
Energy Sharing

This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless charging mechanisms are essential for delivering energy sources from charging towers (i.e., charging infrastructure) to their associated UAVs for seamless operations of autonomous UAVs in the sky. In order to actively and intelligently manage the energy resources in charging towers, a MADRL-based coordinated energy management system is desired and proposed for energy resource sharing among charging towers. When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high costs. Therefore, the main objective of our proposed coordinated MADRL-based energy sharing learning algorithm is minimizing energy purchase from external utility companies to minimize system-operational costs. Finally, our performance evaluation results verify that the proposed coordinated MADRL-based algorithm achieves desired performance improvements.

Improvement on Supporting Machine Learning Algorithm for Solving Problem in Immediate Decision Making

2012 ◽  
Vol 566 ◽  
pp. 572-579
Author(s):  
Abdolkarim Niazi ◽  
Norizah Redzuan ◽  
Raja Ishak Raja Hamzah ◽  
Sara Esfandiari
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Combined Model ◽  
Q Learning ◽  
Agent Systems ◽  
Multi Agent ◽  
Case Base ◽  
Case Base Reasoning ◽  
Robotic Tool

In this paper, a new algorithm based on case base reasoning and reinforcement learning (RL) is proposed to increase the convergence rate of the reinforcement learning algorithms. RL algorithms are very useful for solving wide variety decision problems when their models are not available and they must make decision correctly in every state of system, such as multi agent systems, artificial control systems, robotic, tool condition monitoring and etc. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function is proposed to select the action, which led to an increase in algorithms based on Q-learning. The algorithm mentioned was used for solving the problem of cooperative Markov’s games as one of the models of Markov based multi-agent systems. The results of experiments Indicated that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

A Novel Distributed Multi-Agent Reinforcement Learning Algorithm Against Jamming Attacks

2021 ◽  
Vol 25 (10) ◽  
pp. 3204-3208
Author(s):  
Ibrahim Elleuch ◽  
Ali Pourranjbar ◽  
Georges Kaddoum
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Jamming Attacks ◽  
Multi Agent ◽  
Reinforcement Learning Algorithm

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

2020 ◽  
Vol 17 (2) ◽  
pp. 647-664
Author(s):  
Yangyang Ge ◽  
Fei Zhu ◽  
Wei Huang ◽  
Peiyao Zhao ◽  
Quan Liu
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Multi Agent System ◽  
Agent System ◽  
Action Function ◽  
Q Learning ◽  
State Action ◽  
Markov Game ◽  
Safety Constraints ◽  
Multi Agent

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.

scholarly journals Multiagent reinforcement learning using Non-Parametric Approximation

Respuestas ◽  
2018 ◽  
Vol 23 (2) ◽  
pp. 53-61
Author(s):  
David Luviano Cruz ◽  
Francesco José García Luna ◽  
Luis Asunción Pérez Domínguez
Keyword(s):  
Reinforcement Learning ◽  
Hybrid Control ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Generation Task ◽  
Q Learning ◽  
Agent Systems ◽  
Multi Agent ◽  
Optimal Set ◽  
Parametric Approximation

This paper presents a hybrid control proposal for multi-agent systems, where the advantages of the reinforcement learning and nonparametric functions are exploited. A modified version of the Q-learning algorithm is used which will provide data training for a Kernel, this approach will provide a sub optimal set of actions to be used by the agents. The proposed algorithm is experimentally tested in a path generation task in an unknown environment for mobile robots.

scholarly journals Research on Tensor-Based Cooperative and Competitive in Multi-Agent Reinforcement Learning

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Tsega Weldu Araya ◽  
Md Rashed Ibn Nawab ◽  
A. P. Yuan Ling
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Data Representation ◽  
Training Data ◽  
Two Dimensional ◽  
Multiple Agents ◽  
Learning Agents ◽  
Dimensional Array ◽  
Multi Agent ◽  
Agent Cooperation

As technology overgrows, the assortment of information and the density of work becomes demanding to manage. To resolve the density of employment and human labor, machine-learning (ML) technology developed. Reinforcement learning (RL) is the recent advancement of ML studies. Multi-agent reinforcement learning (MARL) is useful to train multiple agents in the surrounding environment. The previous research studies focused on two-agent cooperation. Their data representation was held in a two-dimensional array, which is called a matrix. The limitation of this two-dimensional array appears as the training data of agents increases. The growth in the training data of agents creates storage drawbacks and data redundancy. Our first aim in this research is to improve an algorithm that can represent MARL training in tensor. In MARL, multiple agents are work together to achieve joint work. To share the training records and data of numerous agents, we need to collect the previous cumulative experience of agents in tensor. Secondly, we will discover the agent's cooperation and competition, with local and global goals of agents in MARL. Local goals are the cooperation of agents in a group or team where we use the training model as a student and teacher agent. The global goal is the competition between two contrary teams to acquire the reward. All learning agents have their Q table for storing the individual agent's training data in an environment. The growth in the number of learning agents, their training experience in Q tables, and the requirement for representing multiple data become the most challenging issue. We introduce tensor to store various data to resolve the challenges for data representation in multiple agent associations. Tensor is expressed as the three-dimensional array, although it is an N-way array, which is useful for representing and accessing numerous data. Finally, we will implement an algorithm for learning three cooperative agents against the opposed team using a tensor-based framework in the Q learning algorithm. We will provide an algorithm that can store the training records and data of multiple agents. Tensor advances to get a small storage size than the matrix for the training records of agents. Although three agent cooperation benefits to having maximum optimal reward.

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