Evolving Equilibrium Policies for a Multiagent Reinforcement Learning Problem with State Attractors

2011 ◽  
pp. 201-210 ◽  
Author(s):  
Florin Leon
Keyword(s):  
Reinforcement Learning ◽  
Learning Problem ◽  
Multiagent Reinforcement Learning

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.

scholarly journals Temporal Uncertainty During Overshadowing

2011 ◽  
pp. 46-55
Author(s):  
Dómhnall J. Jennings ◽  
Eduardo Alonso ◽  
Esther Mondragón ◽  
Charlotte Bonardi
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Associative Learning ◽  
Learning Theories ◽  
Temporal Difference ◽  
Temporal Uncertainty ◽  
Learning Problem ◽  
Difference Model ◽  
Distribution Form ◽  
Temporal Properties

Standard associative learning theories typically fail to conceptualise the temporal properties of a stimulus, and hence cannot easily make predictions about the effects such properties might have on the magnitude of conditioning phenomena. Despite this, in intuitive terms we might expect that the temporal properties of a stimulus that is paired with some outcome to be important. In particular, there is no previous research addressing the way that fixed or variable duration stimuli can affect overshadowing. In this chapter we report results which show that the degree of overshadowing depends on the distribution form - fixed or variable - of the overshadowing stimulus, and argue that conditioning is weaker under conditions of temporal uncertainty. These results are discussed in terms of models of conditioning and timing. We conclude that the temporal difference model, which has been extensively applied to the reinforcement learning problem in machine learning, accounts for the key findings of our study.

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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