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It enables devices to offload compute-intensive tasks to nearby MEC servers. Hence, improving users\u2019 quality of experience (QoS) by reducing both application execution time and energy consumption. However, to meet the huge demands, efficient resource scheduling algorithms are an essential and challenging problem. Resource scheduling involves efficiently allocating and managing MEC resources. In this paper, we survey the state-of-the-art research regarding this issue and focus on deep reinforcement learning (DRL) solutions. DRL algorithms reach optimal or near-optimal policies when adapted to a particular scenario. To the best of our knowledge, this is the first survey that specifically focuses on the use of RL and DRL techniques for resource scheduling in multi-access computing. We analyze recent literature in three research aspects, namely, content caching, computation offloading, and resource management. Moreover, we compare and classify the reviewed papers in terms of application use cases, network architectures, objectives, utilized RL algorithms, evaluation metrics, and model approaches: centralized and distributed. Furthermore, we investigate the issue of user mobility and its effect on the model. Finally, we point out a few unresolved research challenges and suggest several open research topics for future studies.<\/jats:p>","DOI":"10.1007\/s10586-024-04893-7","type":"journal-article","created":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T10:25:45Z","timestamp":1737455145000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["A survey on resource scheduling approaches in multi-access edge computing environment: a deep reinforcement learning study"],"prefix":"10.1007","volume":"28","author":[{"given":"Ahmed A.","family":"Ismail","sequence":"first","affiliation":[]},{"given":"Nour Eldeen","family":"Khalifa","sequence":"additional","affiliation":[]},{"given":"Reda A.","family":"El-Khoribi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,21]]},"reference":[{"key":"4893_CR1","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1109\/COMST.2021.3073036","volume":"23","author":"W Chen","year":"2021","unstructured":"Chen, W., Qiu, X., Cai, T., Dai, H.-N., Zheng, Z., Zhang, Y.: Deep reinforcement learning for internet of things: a comprehensive survey. 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