Multi-Agent Reinforcement Learning using the Deep Distributed Distributional Deterministic Policy Gradients Algorithm

In this paper, the Deep Distributed Distributional Deterministic Policy Gradients (D4PG) reinforcement learning algorithm is adopted to train a multi-agent action in a cooperative game environment. The algorithm is experimented on training the agents to play a game of tennis against each other. The...

Full description

Saved in:
Bibliographic Details
Published in:2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT) pp. 1 - 6
Main Author: Farag, Wael
Format: Conference Proceeding
Language:English
Published: IEEE 20.12.2020
Subjects:
D4PG
Games
Informatics
Machine Learning
Machine learning algorithms
Multi-Agent
Policy-Gradients Methods
Reinforcement learning
Task analysis
Technological innovation
Training
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract In this paper, the Deep Distributed Distributional Deterministic Policy Gradients (D4PG) reinforcement learning algorithm is adopted to train a multi-agent action in a cooperative game environment. The algorithm is experimented on training the agents to play a game of tennis against each other. The architectures of the actor and cretic networks are meticulously designed and the D4PG hyperparameters are carefully tuned. The trained agents are successfully tested in the Unity Machine Learning Agents environment. The testing shows the powerful performance of the D4PG algorithm in training multi-agents in complex environments.
AbstractList In this paper, the Deep Distributed Distributional Deterministic Policy Gradients (D4PG) reinforcement learning algorithm is adopted to train a multi-agent action in a cooperative game environment. The algorithm is experimented on training the agents to play a game of tennis against each other. The architectures of the actor and cretic networks are meticulously designed and the D4PG hyperparameters are carefully tuned. The trained agents are successfully tested in the Unity Machine Learning Agents environment. The testing shows the powerful performance of the D4PG algorithm in training multi-agents in complex environments.
Author Farag, Wael
Author_xml – sequence: 1
  givenname: Wael
  surname: Farag
  fullname: Farag, Wael
  email: wael.farag@aum.edu.kw
  organization: American University of the Middle East,College of Eng. & Tech.,Kuwait City,Kuwait
BookMark eNpFUN1KwzAYjaAXOvcEguQFOvslbZpelk3noKLIvB5N-rX7oE1Hml7s7e1w4M35hXNxHtitGxwy9gzxCiDOX-RuvU8BErUSsYhXuQTIk_SGLfNMQyY05CqT4p5NH1MXKCpadIF_I7lm8Bb7iyux8o5cy6fxguGIfIN44hsagyczBaz_NQ2u6uY-oO_JzSlZ_jV0ZM9866ua5sGRF107eArH_pHdNVU34vLKC_bz9rpfv0fl53a3LsqIAHSItMVUCEhrYbU0dSwbYWJl8lTbukbUogEpDCZzDBqlUkJZkxhlNWYKrZAL9vS3S4h4OHnqK38-XM-Qvy01XI8
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/3ICT51146.2020.9311945
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9781728196732
1728196736
EndPage 6
ExternalDocumentID 9311945
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i118t-8ce52215d2c83bd03f2b06b958cddee82f132be4f2b18e36626cb4b6c8e76ec23
IEDL.DBID RIE
IngestDate Thu Jun 29 18:38:04 EDT 2023
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i118t-8ce52215d2c83bd03f2b06b958cddee82f132be4f2b18e36626cb4b6c8e76ec23
PageCount 6
ParticipantIDs ieee_primary_9311945
PublicationCentury 2000
PublicationDate 2020-Dec.-20
PublicationDateYYYYMMDD 2020-12-20
PublicationDate_xml – month: 12
  year: 2020
  text: 2020-Dec.-20
  day: 20
PublicationDecade 2020
PublicationTitle 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT)
PublicationTitleAbbrev 3ICT
PublicationYear 2020
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.7911773
Snippet In this paper, the Deep Distributed Distributional Deterministic Policy Gradients (D4PG) reinforcement learning algorithm is adopted to train a multi-agent...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms D4PG
Games
Informatics
Machine Learning
Machine learning algorithms
Multi-Agent
Policy-Gradients Methods
Reinforcement learning
Task analysis
Technological innovation
Training
Title Multi-Agent Reinforcement Learning using the Deep Distributed Distributional Deterministic Policy Gradients Algorithm
URI https://ieeexplore.ieee.org/document/9311945
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1BT8IwFG6AePCkBoyKmh48OujabW2PBERNDCEGDTdCuzckUSBj-Pt97RaIiRdvXddkSZu173v9vu8RcjdPlUiiTAQpN_6a0eAvhY9ahpmRzGrmy7e9v8jRSE2nelwj93stDAB48hl0XNPf5adru3Opsq4WIWLuuE7qUialVqsS_YZMd8VzfxI7kS2iPs461eBfVVP8oTE8-d_nTknroL6j4_25ckZqsGqSnVfKBj2nhKKv4P1OrU_t0coidUEdh31BMaKjA4ANHThPXFfOCtJD22f-8H1JgvEuzbT0BqaPuad_FVva-1ys82Xx8dUib8OHSf8pqEomBEtECkWgLGBAFcYpt0qYlImMG5YYHSuL-xgoniH6NBBhd6hAJAhnrIlMYhXIBCwX56SxWq_ggtCQZ9LGfG5shlGdDo3AWGOuXEgWIYbTl6Tppmy2KV0xZtVsXf3d3SbHblUcEYSza9Io8h3ckCP7XSy3-a1fyh9XyaNd
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NTwIxEJ0gmuhJDRi_7cGjC912P9ojAREiEmLQcCO0O4skCmRZ_P22ZQMx8eKt222ySZtt503fewNwP0kEj4KUewlT7ppRmV_KPMrYT1VMtaSufNt7L-73xWgkByV42GphENGRz7Bmm-4uP1notU2V1SX3DeYO92DfVs4q1FqF7Nenss67zWFoZbYG9zFaK4b_qpvijo328f8-eALVnf6ODLYnyymUcF6BtdPKeg2rhSKv6BxPtUvukcIkdUosi31KTExHWohL0rKuuLagFSa7tsv9mfcbGozzaSYbd2DylDkCWL4ijc_pIpvlH19VeGs_Dpsdryia4M0MVsg9odGEVH6YMC24SihPmaKRkqHQZidDwVKDPxUGptsXyCMDaLQKVKQFxhFqxs-gPF_M8RyIz9JYh2yidGriOukrbqKNibBBWWBQnLyAip2y8XLjizEuZuvy7-47OOwMX3rjXrf_fAVHdoUsLYTRayjn2Rpv4EB_57NVduuW9QcE8qam
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2020+International+Conference+on+Innovation+and+Intelligence+for+Informatics%2C+Computing+and+Technologies+%283ICT%29&rft.atitle=Multi-Agent+Reinforcement+Learning+using+the+Deep+Distributed+Distributional+Deterministic+Policy+Gradients+Algorithm&rft.au=Farag%2C+Wael&rft.date=2020-12-20&rft.pub=IEEE&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2F3ICT51146.2020.9311945&rft.externalDocID=9311945