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Scalable Deep Reinforcement Learning for a Multi-Agent Warehouse System

Kandidat-uppsats

Khan, Akib, Loberg, Marcus

KTH/Skolan för elektroteknik och datavetenskap (EECS)

Publicerad: 2022

Språk: Engelska

Nyckelord

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⌕Reinforcement Learning⌕Neural Network⌕scalability⌕Warehouse Environment⌕Multi-Agents⌕Deep Q Networks

Sammanfattning

This report presents an application of reinforcementlearning to the problem of controlling multiple robots performingthe task of moving boxes in a warehouse environment. The robotsmake autonomous decisions individually and avoid colliding witheach other and the walls of the warehouse. The problem is definedas a dynamical multi-agent system and a solution is reachedby applying the DQN algorithm. The solution is designed forachieving scalability, meaning that the trained robots are flexibleenough to be deployed in simulated environments of differentsizes and alongside a different number of robots. This wassuccessfully achieved by feature engineering.

Information

Författare
Khan, Akib, Loberg, Marcus
Lärosäte / institution
KTH/Skolan för elektroteknik och datavetenskap (EECS)
Publiceringsdatum
2022
Uppsatstyp
Kandidat-uppsats
Språk
Engelska
Nyckelord
⌕Reinforcement Learning⌕Neural Network⌕scalability⌕Warehouse Environment⌕Multi-Agents⌕Deep Q Networks

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