Opponent portrait for multiagent reinforcement learning in competitive environment

2021 ◽  
Author(s):  
Yuxi Ma ◽  
Meng Shen ◽  
Yuhang Zhao ◽  
Zhao Li ◽  
Xiaoyao Tong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Competitive Environment ◽  
Multiagent Reinforcement Learning

scholarly journals Methods and Algorithms for Knowledge Reuse in Multiagent Reinforcement Learning

2020 ◽  
Author(s):  
Felipe Leno Da Silva ◽  
Anna Helena Reali Costa
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Learning Process ◽  
Trial And Error ◽  
Knowledge Reuse ◽  
Previous Knowledge ◽  
Learning Methods ◽  
Types Of Knowledge ◽  
Learning Agent ◽  
Multiagent Reinforcement Learning

Reinforcement Learning (RL) is a powerful tool that has been used to solve increasingly complex tasks. RL operates through repeated interactions of the learning agent with the environment, via trial and error. However, this learning process is extremely slow, requiring many interactions. In this thesis, we leverage previous knowledge so as to accelerate learning in multiagent RL problems. We propose knowledge reuse both from previous tasks and from other agents. Several flexible methods are introduced so that each of these two types of knowledge reuse is possible. This thesis adds important steps towards more flexible and broadly applicable multiagent transfer learning methods.

Multiagent Reinforcement Learning

2021 ◽  
pp. 1359-1367
Author(s):  
Jonathan P. How ◽  
Dong-Ki Kim ◽  
Samir Wadhwania
Keyword(s):  
Reinforcement Learning ◽  
Multiagent Reinforcement Learning
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