Collaborative Intelligent Multi-agent Reinforcement Learning over a Network
draft-kim-nmlrg-network-00
| Document | Type |
Expired Internet-Draft
(individual)
Expired & archived
|
|
|---|---|---|---|
| Authors | Min-Suk Kim , Yong-Geun Hong | ||
| Last updated | 2017-09-14 (Latest revision 2017-03-13) | ||
| RFC stream | (None) | ||
| Intended RFC status | (None) | ||
| Formats | |||
| Stream | Stream state | (No stream defined) | |
| Consensus boilerplate | Unknown | ||
| RFC Editor Note | (None) | ||
| IESG | IESG state | Expired | |
| Telechat date | (None) | ||
| Responsible AD | (None) | ||
| Send notices to | (None) |
This Internet-Draft is no longer active. A copy of the expired Internet-Draft is available in these formats:
Abstract
This document describes agent reinforcement learning (RL) in a distributed environment to transfer or share information for autonomous shortest path-planning over a communication network. The centralized node, which is the main node to manage agent workflow in hybrid peer-to-peer environment, provides a cumulative reward for each action that a given agent takes with respect to an optimal path based on a to-be-learned policy over the learning process. A reward from the centralized node is reflected when an agent explores to reach its destination for autonomous shortest path-planning in distributed nodes.
Authors
(Note: The e-mail addresses provided for the authors of this Internet-Draft may no longer be valid.)