Social interaction of cooperative communication and group generation in multi-agent reinforcement learning systems

2011 ◽  
Author(s):  
Kun Zhang ◽  
Yoichiro Maeda ◽  
Yasutake Takahashi
Keyword(s):  
Reinforcement Learning ◽  
Social Interaction ◽  
Cooperative Communication ◽  
Learning Systems ◽  
Multi Agent ◽  
Group Generation

scholarly journals Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 6637-6644
Author(s):  
H. El Fazazi ◽  
M. Elgarej ◽  
M. Qbadou ◽  
K. Mansouri
Keyword(s):  
Reinforcement Learning ◽  
Learning Style ◽  
Learning Process ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
E Learning ◽  
Multi Agent

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.

scholarly journals Attention-Based Fault-Tolerant Approach for Multi-Agent Reinforcement Learning Systems

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1133
Author(s):  
Shanzhi Gu ◽  
Mingyang Geng ◽  
Long Lan
Keyword(s):  
Reinforcement Learning ◽  
Noise Intensity ◽  
Fault Tolerant ◽  
State Of The Art ◽  
Learning Systems ◽  
Noisy Environments ◽  
Time Step ◽  
Malicious Behavior ◽  
Previous State ◽  
Multi Agent

The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. Typically, an agent receives its private observations providing a partial view of the true state of the environment. However, in realistic settings, the harsh environment might cause one or more agents to show arbitrarily faulty or malicious behavior, which may suffice to allow the current coordination mechanisms fail. In this paper, we study a practical scenario of multi-agent reinforcement learning systems considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. The previous state-of-the-art work that coped with extremely noisy environments was designed on the basis that the noise intensity in the environment was known in advance. However, when the noise intensity changes, the existing method has to adjust the configuration of the model to learn in new environments, which limits the practical applications. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) model, which can select not only correct, but also relevant information for each agent at every time step in noisy environments. The multihead attention mechanism enables the agents to learn effective communication policies through experience concurrent with the action policies. Empirical results showed that FT-Attn beats previous state-of-the-art methods in some extremely noisy environments in both cooperative and competitive scenarios, much closer to the upper-bound performance. Furthermore, FT-Attn maintains a more general fault tolerance ability and does not rely on the prior knowledge about the noise intensity of the environment.

scholarly journals Formal Reachability Analysis for Multi-Agent Reinforcement Learning Systems

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45812-45821
Author(s):  
Xiaoyan Wang ◽  
Jun Peng ◽  
Shuqiu Li ◽  
Bing Li
Keyword(s):  
Reinforcement Learning ◽  
Reachability Analysis ◽  
Learning Systems ◽  
Multi Agent

Exploitation-Oriented Learning XoL

2010 ◽  
pp. 267-293 ◽  
Author(s):  
Kazuteru Miyazaki
Keyword(s):  
Reinforcement Learning ◽  
Optimal Policy ◽  
Profit Sharing ◽  
Learning Systems ◽  
Numerical Examples ◽  
Multi Agent ◽  
New Framework

Exploitation-oriented Learning XoL is a new framework of reinforcement learning. XoL aims to learn a rational policy whose expected reward per an action is larger than zero, and does not require a sophisticated design of the value of a reward signal. In this chapter, as examples of learning systems that belongs in XoL, we introduce the rationality theorem of profit Sharing (PS), the rationality theorem of reward sharing in multi-agent PS, and PS-r*. XoL has several features. (1) Though traditional RL systems require appropriate reward and penalty values, XoL only requires an order of importance among them. (2) XoL can learn more quickly since it traces successful experiences very strongly. (3) XoL may be unsuitable for pursuing an optimal policy. The optimal policy can be acquired by the multi-start method that needs to reset all memories to get a better policy. (4) XoL is effective on the classes beyond MDPs, since it is a Bellman-free method that does not depend on DP. We show several numerical examples to confirm these features.

Multi-agent deep reinforcement learning with type-based hierarchical group communication

2021 ◽  
Author(s):  
Hao Jiang ◽  
Dianxi Shi ◽  
Chao Xue ◽  
Yajie Wang ◽  
Gongju Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Group Communication ◽  
Multi Agent ◽  
Hierarchical Group
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