Deep reinforcement learning to multi-agent deep reinforcement learning
Machine Learning (ML) has been a remarkable success in the last few years, which Reinforcement Learning (RL) has seen rapid growth with new techniques that have revolutionized the area. Sequential -Decision Making tasks are a main topic in ML, these are tasks based on deciding, the sequence of actio...
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| פורמט: | Article |
| יצא לאור: |
Little Lion Scientific
2022
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| _version_ | 1825938451243991040 |
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| author | Samieiyeganeh, Mehdi O. K. Rahmat, Rahmita Wirza Khalid, Fatimah Kasmiran, Khairul Azhar |
| author_facet | Samieiyeganeh, Mehdi O. K. Rahmat, Rahmita Wirza Khalid, Fatimah Kasmiran, Khairul Azhar |
| author_sort | Samieiyeganeh, Mehdi |
| collection | UPM |
| description | Machine Learning (ML) has been a remarkable success in the last few years, which Reinforcement Learning (RL) has seen rapid growth with new techniques that have revolutionized the area. Sequential -Decision Making tasks are a main topic in ML, these are tasks based on deciding, the sequence of actions from experience carry out in an environment that is uncertain to achieve goals In this paper, we discuss topics such as Deep Learning (DL) and Multi-agent Systems (MAS) that are used in RL as Deep Reinforcement Learning (DRL) and Multi - Agent Deep Reinforcement Learning (MADRL). In fact, overall goal in this paper is a comprehensive explanation of the various Deep Reinforcement Learning (DRL) algorithms, and its combination with Multi-Agent methods. To achieve this goal, in section 2, we have reviewed the articles that are the founders of these methods and have also used various methods in the field of MADRL. In the third section, we look at the RL and important algorithms that exist in this area. In the fourth section, we study DRL and explain the reasons for which different algorithms have been developed in this regard. In the fifth section, we will look at the MADRL and address some of the challenges and work that has been done in this area..At the end of this section we mentioned some important papers in the table with their methods, which is used. The sixth section provides an explanation of the research currently being done by the authors, as well as interesting topics for researchers to use in future research. Given that we have tried to explain the concepts in a simple and straightforward way in this paper, we hope that the materials mentioned are suitable for novice researchers in this field. |
| first_indexed | 2024-03-06T11:14:04Z |
| format | Article |
| id | upm.eprints-100877 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:14:04Z |
| publishDate | 2022 |
| publisher | Little Lion Scientific |
| record_format | dspace |
| spelling | upm.eprints-1008772023-07-26T02:56:42Z http://psasir.upm.edu.my/id/eprint/100877/ Deep reinforcement learning to multi-agent deep reinforcement learning Samieiyeganeh, Mehdi O. K. Rahmat, Rahmita Wirza Khalid, Fatimah Kasmiran, Khairul Azhar Machine Learning (ML) has been a remarkable success in the last few years, which Reinforcement Learning (RL) has seen rapid growth with new techniques that have revolutionized the area. Sequential -Decision Making tasks are a main topic in ML, these are tasks based on deciding, the sequence of actions from experience carry out in an environment that is uncertain to achieve goals In this paper, we discuss topics such as Deep Learning (DL) and Multi-agent Systems (MAS) that are used in RL as Deep Reinforcement Learning (DRL) and Multi - Agent Deep Reinforcement Learning (MADRL). In fact, overall goal in this paper is a comprehensive explanation of the various Deep Reinforcement Learning (DRL) algorithms, and its combination with Multi-Agent methods. To achieve this goal, in section 2, we have reviewed the articles that are the founders of these methods and have also used various methods in the field of MADRL. In the third section, we look at the RL and important algorithms that exist in this area. In the fourth section, we study DRL and explain the reasons for which different algorithms have been developed in this regard. In the fifth section, we will look at the MADRL and address some of the challenges and work that has been done in this area..At the end of this section we mentioned some important papers in the table with their methods, which is used. The sixth section provides an explanation of the research currently being done by the authors, as well as interesting topics for researchers to use in future research. Given that we have tried to explain the concepts in a simple and straightforward way in this paper, we hope that the materials mentioned are suitable for novice researchers in this field. Little Lion Scientific 2022-02-28 Article PeerReviewed Samieiyeganeh, Mehdi and O. K. Rahmat, Rahmita Wirza and Khalid, Fatimah and Kasmiran, Khairul Azhar (2022) Deep reinforcement learning to multi-agent deep reinforcement learning. Journal of Theoretical and Applied Information Technology, 100 (4). 990 - 1003. ISSN 1992-8645; ESSN: 1817-3195 http://www.jatit.org/volumes/onehundred04.php |
| spellingShingle | Samieiyeganeh, Mehdi O. K. Rahmat, Rahmita Wirza Khalid, Fatimah Kasmiran, Khairul Azhar Deep reinforcement learning to multi-agent deep reinforcement learning |
| title | Deep reinforcement learning to multi-agent deep reinforcement learning |
| title_full | Deep reinforcement learning to multi-agent deep reinforcement learning |
| title_fullStr | Deep reinforcement learning to multi-agent deep reinforcement learning |
| title_full_unstemmed | Deep reinforcement learning to multi-agent deep reinforcement learning |
| title_short | Deep reinforcement learning to multi-agent deep reinforcement learning |
| title_sort | deep reinforcement learning to multi agent deep reinforcement learning |
| work_keys_str_mv | AT samieiyeganehmehdi deepreinforcementlearningtomultiagentdeepreinforcementlearning AT okrahmatrahmitawirza deepreinforcementlearningtomultiagentdeepreinforcementlearning AT khalidfatimah deepreinforcementlearningtomultiagentdeepreinforcementlearning AT kasmirankhairulazhar deepreinforcementlearningtomultiagentdeepreinforcementlearning |