Paper Info
Title
Prediction-Based Multi-Agent Reinforcement Learning in Inherently Non-Stationary Environments.
Abstract
Multi-agent reinforcement learning (MARL) is a widely researched technique for decentralised control in complex large-scale autonomous systems. Such systems often operate in environments that are continuously evolving and where agents’ actions are non-deterministic, so called inherently non-stationary environments. When there are inconsistent results for agents acting on such an environment, learning and adapting is challenging. In this article, we propose P-MARL, an approach that integrates prediction and pattern change detection abilities into MARL and thus minimises the effect of non-stationarity in the environment. The environment is modelled as a time-series, with future estimates provided using prediction techniques. Learning is based on the predicted environment behaviour, with agents employing this knowledge to improve their performance in realtime. We illustrate P-MARL’s performance in a real-world smart grid scenario, where the environment is heavily influenced by non-stationary power demand patterns from residential consumers. We evaluate P-MARL in three different situations, where agents’ action decisions are independent, simultaneous, and sequential. Results show that all methods outperform traditional MARL, with sequential P-MARL achieving best results.
Year
DOI
Venue
2017
10.1145/3070861
TAAS
Keywords
Field
DocType
Multi-agent systems,reinforcement learning,environment prediction,smart grids
Change detection,Smart grid,Computer science,Multi-agent system,Power demand,Artificial intelligence,Autonomous system (Internet),Error-driven learning,Reinforcement learning,Distributed computing
Journal
Volume
Issue
ISSN
12
2
1556-4665
Citations 
PageRank 
References 
4
0.44
29
Authors
3
Name
Order
Citations
PageRank
Andrei Marinescu1214.29
Ivana Dusparic27520.37
Siobhán Clarke369987.36