scholarly journals Transfer Learning in Multi-Agent Reinforcement Learning Domains

2012 ◽  
pp. 249-260 ◽  
Author(s):  
Georgios Boutsioukis ◽  
Ioannis Partalas ◽  
Ioannis Vlahavas
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Learning Domains ◽  
Multi Agent

scholarly journals An Introduction to Intertask Transfer for Reinforcement Learning

AI Magazine ◽  
2011 ◽  
Vol 32 (1) ◽  
pp. 15 ◽  
Author(s):  
Matthew E. Taylor ◽  
Peter Stone
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Learning Problem ◽  
Open Problems ◽  
Learning Framework ◽  
Learning Domains ◽  
Multiple Tasks ◽  
Exciting Area ◽  
Generalize Information ◽  
Selection Of

Transfer learning has recently gained popularity due to the development of algorithms that can successfully generalize information across multiple tasks. This article focuses on transfer in the context of reinforcement learning domains, a general learning framework where an agent acts in an environment to maximize a reward signal. The goals of this article are to (1) familiarize readers with the transfer learning problem in reinforcement learning domains, (2) explain why the problem is both interesting and difficult, (3) present a selection of existing techniques that demonstrate different solutions, and (4) provide representative open problems in the hope of encouraging additional research in this exciting area.

scholarly journals Transfer Learning Method Using Ontology for Heterogeneous Multi-agent Reinforcement Learning

2014 ◽  
Vol 5 (10) ◽  
Author(s):  
Hitoshi Kono ◽  
Akiya Kamimura ◽  
Kohji Tomita ◽  
Yuta Murata ◽  
Tsuyoshi Suzuki
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Learning Method ◽  
Multi Agent

scholarly journals Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 341 ◽  
Author(s):  
Hu ◽  
Xu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
Action Function ◽  
State Action ◽  
Markov Decision ◽  
Decision Making System ◽  
Multi Agent ◽  
Function Approximator ◽  
Multi Robot

Multi-Robot Confrontation on physics-based simulators is a complex and time-consuming task, but simulators are required to evaluate the performance of the advanced algorithms. Recently, a few advanced algorithms have been able to produce considerably complex levels in the context of the robot confrontation system when the agents are facing multiple opponents. Meanwhile, the current confrontation decision-making system suffers from difficulties in optimization and generalization. In this paper, a fuzzy reinforcement learning (RL) and the curriculum transfer learning are applied to the micromanagement for robot confrontation system. Firstly, an improved Qlearning in the semi-Markov decision-making process is designed to train the agent and an efficient RL model is defined to avoid the curse of dimensionality. Secondly, a multi-agent RL algorithm with parameter sharing is proposed to train the agents. We use a neural network with adaptive momentum acceleration as a function approximator to estimate the state-action function. Then, a method of fuzzy logic is used to regulate the learning rate of RL. Thirdly, a curriculum transfer learning method is used to extend the RL model to more difficult scenarios, which ensures the generalization of the decision-making system. The experimental results show that the proposed method is effective.

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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