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Keyword: Reinforcement Learning
Modeling Control Agents in Social Media Networks Using Reinforcement Learning
Designing efficient control strategies for opinion dynamics is a challenging task. Understanding how individuals change their opinions in social networks is essential to countering malicious actors and fake news and mitigating their effect on the network. In many applications such as marketing design, product launches, etc., corporations often post curated news or feeds on social…
Read More A New Technique to Accelerate the Learning Process in Agents based on Reinforcement Learning
The use of decentralized reinforcement learning (RL) in the context of multi-agent systems (MAS) poses some difficult problems. The speed of the learning process for example. Indeed, if the convergence of these algorithms has been widely studied and mathematically proven, they suffer from being very slow. In this context, we propose to use RL in…
Read More A New Distributed Reinforcement Learning Approach for Multiagent Cooperation Using Team-mate Modeling and Joint Action Generalization
This paper focuses on the issue of distributed reinforcement learning (RL) for decision-making in cooperative multi-agent systems. Although this problem has been a topic of interest to many researchers, results obtained from these works aren’t sufficient and several difficulties have not yet been resolved, such as, the curse of dimensionality and the multi-agent coordination problem.…
Read More Hypervolume-Based Multi-Objective Reinforcement Learning: Interactive Approach
In this paper, we propose a procedure of interactive multi-objective reinforcement learning for multi-step decision problems based on the preference of a decision maker. The proposed method is constructed based on the multi-objective reinforcement learning which is applied to multi-step multi-objective optimization problems. The existing literature related to the multi-objective reinforcement learning indicate that the…
Read More Boltzmann-Based Distributed Control Method: An Evolutionary Approach using Neighboring Population Constraints
In control systems, several optimization problems have been overcome using Multi-Agent Sys- tems (MAS). Interactions of agents and the complexity of the system can be understood by using MAS. As a result, functional models are generated, which are closer to reality. Nevertheless, the use of models with permanent availability of information between agents is assumed…
Read More Deep Deterministic Policy Gradients for Optimizing Simulated PoA Blockchain Networks Based on Healthcare Data Characteristics
Blockchain technology has proven to be the best solution for digital data storage today, which is decentralized and interconnected via cryptography. Many consensus algorithms can be options for implementation. One of them is the PoA consensus algorithm, which is proven to provide high performance and fault tolerance. Blockchain has been implemented in many sectors, including…
Read More A Study on Intelligent Dialogue Agent for Older Adults’ Preventive Care – Towards Development of a Comprehensive Preventive Care System
Preventive care approaches have attracted much attention in Japan, which is one of the world’s most super-aged societies. These approaches aim to decrease the number of people who require nursing care or other human support. Our research group has developed several kinds of preventive care systems, including a fall prevention system, a cognitive training system,…
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