scholarly journals Balancing Individual Preferences and Shared Objectives in Multiagent Reinforcement Learning

2020 ◽  
Author(s):  
Ishan Durugkar ◽  
Elad Liebman ◽  
Peter Stone
Keyword(s):  
Reinforcement Learning ◽  
Theoretical Analysis ◽  
Shared Task ◽  
Individual Preferences ◽  
Multiagent Reinforcement Learning ◽  
Learning Scenarios ◽  
Mixing Schemes ◽  
Task Oriented

In multiagent reinforcement learning scenarios, it is often the case that independent agents must jointly learn to perform a cooperative task. This paper focuses on such a scenario in which agents have individual preferences regarding how to accomplish the shared task. We consider a framework for this setting which balances individual preferences against task rewards using a linear mixing scheme. In our theoretical analysis we establish that agents can reach an equilibrium that leads to optimal shared task reward even when they consider individual preferences which aren't fully aligned with this task. We then empirically show, somewhat counter-intuitively, that there exist mixing schemes that outperform a purely task-oriented baseline. We further consider empirically how to optimize the mixing scheme.

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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