Reinforcement Learning with Side Information for the Uncertainties
Recently, there has been a growing interest in the consensus of a multi-agent system (MAS) with advances in artificial intelligence and distributed computing. Sliding mode control (SMC) is a well-known method that provides robust control in the presence of uncertainties. While our previous study int...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/24/9811 |
| _version_ | 1797455327361236992 |
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| author | Janghoon Yang |
| author_facet | Janghoon Yang |
| author_sort | Janghoon Yang |
| collection | DOAJ |
| description | Recently, there has been a growing interest in the consensus of a multi-agent system (MAS) with advances in artificial intelligence and distributed computing. Sliding mode control (SMC) is a well-known method that provides robust control in the presence of uncertainties. While our previous study introduced SMC to the reinforcement learning (RL) based on approximate dynamic programming in the context of optimal control, SMC is introduced to a conventional RL framework in this work. As a specific realization, the modified twin delayed deep deterministic policy gradient (DDPG) for consensus was exploited to develop sliding mode RL. Numerical experiments show that the sliding mode RL outperforms existing state-of-the-art RL methods and model-based methods in terms of the mean square error (MSE) performance. |
| first_indexed | 2024-03-09T15:52:49Z |
| format | Article |
| id | doaj.art-b3603212ddbb468ebca8a3390ce68644 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:49Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b3603212ddbb468ebca8a3390ce686442023-11-24T17:55:43ZengMDPI AGSensors1424-82202022-12-012224981110.3390/s22249811Reinforcement Learning with Side Information for the UncertaintiesJanghoon Yang0Department of A.I. Software Engineering, Seoul Media Institute of Technology, Seoul 07590, Republic of KoreaRecently, there has been a growing interest in the consensus of a multi-agent system (MAS) with advances in artificial intelligence and distributed computing. Sliding mode control (SMC) is a well-known method that provides robust control in the presence of uncertainties. While our previous study introduced SMC to the reinforcement learning (RL) based on approximate dynamic programming in the context of optimal control, SMC is introduced to a conventional RL framework in this work. As a specific realization, the modified twin delayed deep deterministic policy gradient (DDPG) for consensus was exploited to develop sliding mode RL. Numerical experiments show that the sliding mode RL outperforms existing state-of-the-art RL methods and model-based methods in terms of the mean square error (MSE) performance.https://www.mdpi.com/1424-8220/22/24/9811reinforcement learningconsensusmulti-agent systemsliding mode control |
| spellingShingle | Janghoon Yang Reinforcement Learning with Side Information for the Uncertainties Sensors reinforcement learning consensus multi-agent system sliding mode control |
| title | Reinforcement Learning with Side Information for the Uncertainties |
| title_full | Reinforcement Learning with Side Information for the Uncertainties |
| title_fullStr | Reinforcement Learning with Side Information for the Uncertainties |
| title_full_unstemmed | Reinforcement Learning with Side Information for the Uncertainties |
| title_short | Reinforcement Learning with Side Information for the Uncertainties |
| title_sort | reinforcement learning with side information for the uncertainties |
| topic | reinforcement learning consensus multi-agent system sliding mode control |
| url | https://www.mdpi.com/1424-8220/22/24/9811 |
| work_keys_str_mv | AT janghoonyang reinforcementlearningwithsideinformationfortheuncertainties |