Reinforcement Learning via Conservative Agent for Environments with Random Delays
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911412611186688 |
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| author | Lee, Jongsoo Kim, Jangwon Jeong, Jiseok Han, Soohee |
| author_facet | Lee, Jongsoo Kim, Jangwon Jeong, Jiseok Han, Soohee |
| contents | Real-world reinforcement learning applications are often hindered by delayed feedback from environments, which violates the Markov assumption and introduces significant challenges. Although numerous delay-compensating methods have been proposed for environments with constant delays, environments with random delays remain largely unexplored due to their inherent variability and unpredictability. In this study, we propose a simple yet robust agent for decision-making under random delays, termed the conservative agent, which reformulates the random-delay environment into its constant-delay equivalent. This transformation enables any state-of-the-art constant-delay method to be directly extended to the random-delay environments without modifying the algorithmic structure or sacrificing performance. We evaluate the conservative agent-based algorithm on continuous control tasks, and empirical results demonstrate that it significantly outperforms existing baseline algorithms in terms of asymptotic performance and sample efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning via Conservative Agent for Environments with Random Delays Lee, Jongsoo Kim, Jangwon Jeong, Jiseok Han, Soohee Machine Learning Artificial Intelligence Real-world reinforcement learning applications are often hindered by delayed feedback from environments, which violates the Markov assumption and introduces significant challenges. Although numerous delay-compensating methods have been proposed for environments with constant delays, environments with random delays remain largely unexplored due to their inherent variability and unpredictability. In this study, we propose a simple yet robust agent for decision-making under random delays, termed the conservative agent, which reformulates the random-delay environment into its constant-delay equivalent. This transformation enables any state-of-the-art constant-delay method to be directly extended to the random-delay environments without modifying the algorithmic structure or sacrificing performance. We evaluate the conservative agent-based algorithm on continuous control tasks, and empirical results demonstrate that it significantly outperforms existing baseline algorithms in terms of asymptotic performance and sample efficiency. |
| title | Reinforcement Learning via Conservative Agent for Environments with Random Delays |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.18992 |