Federated Reinforcement Learning at the Edge
- URL: http://arxiv.org/abs/2112.05908v1
- Date: Sat, 11 Dec 2021 03:28:59 GMT
- Title: Federated Reinforcement Learning at the Edge
- Authors: Konstantinos Gatsis
- Abstract summary: Modern cyber-physical architectures use data collected from systems at different physical locations to learn appropriate behaviors and adapt to uncertain environments.
This paper considers a setup where multiple agents need to communicate efficiently in order to jointly solve a reinforcement learning problem over time-series data collected in a distributed manner.
An algorithm for achieving communication efficiency is proposed, supported with theoretical guarantees, practical implementations, and numerical evaluations.
- Score: 1.4271989597349055
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Modern cyber-physical architectures use data collected from systems at
different physical locations to learn appropriate behaviors and adapt to
uncertain environments. However, an important challenge arises as communication
exchanges at the edge of networked systems are costly due to limited resources.
This paper considers a setup where multiple agents need to communicate
efficiently in order to jointly solve a reinforcement learning problem over
time-series data collected in a distributed manner. This is posed as learning
an approximate value function over a communication network. An algorithm for
achieving communication efficiency is proposed, supported with theoretical
guarantees, practical implementations, and numerical evaluations. The approach
is based on the idea of communicating only when sufficiently informative data
is collected.
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