scholarly journals Methods and algorithms for knowledge reuse in multiagent reinforcement learning.

2019 ◽  
Author(s):  
Felipe Leno da Silva
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Reuse ◽  
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 A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems

2019 ◽  
Vol 64 ◽  
pp. 645-703 ◽  
Author(s):  
Felipe Leno Da Silva ◽  
Anna Helena Reali Costa
Keyword(s):  
Reinforcement Learning ◽  
Scale Up ◽  
Shared Environment ◽  
Complex Task ◽  
General Knowledge ◽  
Sample Complexity ◽  
Knowledge Reuse ◽  
Open Questions ◽  
Multiagent Reinforcement Learning ◽  
Current Lines

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem, providing a comprehensive discussion of recent progress on knowledge reuse in Multiagent Systems (MAS) and of techniques for knowledge reuse across agents (that may be actuating in a shared environment or not). We aim at encouraging the community to work towards reusing all the knowledge sources available in a MAS. For that, we provide an in-depth discussion of current lines of research and open questions.

scholarly journals Autonomously Reusing Knowledge in Multiagent Reinforcement Learning

2018 ◽  
Author(s):  
Felipe Leno Da Silva ◽  
Matthew E. Taylor ◽  
Anna Helena Reali Costa
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Agents ◽  
Knowledge Reuse ◽  
Challenging Problem ◽  
Multiple Sources ◽  
Relevant Research ◽  
Core Technology ◽  
Multiagent Reinforcement Learning ◽  
Important Challenge ◽  
Research Challenge

Autonomous agents are increasingly required to solve complex tasks; hard-coding behaviors has become infeasible. Hence, agents must learn how to solve tasks via interactions with the environment. In many cases, knowledge reuse will be a core technology to keep training times reasonable, and for that, agents must be able to autonomously and consistently reuse knowledge from multiple sources, including both their own previous internal knowledge and from other agents. In this paper, we provide a literature review of methods for knowledge reuse in Multiagent Reinforcement Learning. We define an important challenge problem for the AI community, survey the existent methods, and discuss how they can all contribute to this challenging problem. Moreover, we highlight gaps in the current literature, motivating "low-hanging fruit'' for those interested in the area. Our ambition is that this paper will encourage the community to work on this difficult and relevant research challenge.

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