mlsanityData to 14 Oct 2026

Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks

arXiv 2111.12064 ↗First posted 23 Nov 2021cs.IT cs.LG cs.NIcited by 6

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Abstract (arXiv)

Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach to enable future intelligent and autonomous systems that rely on real-time decision-making in complex dynamic environments. Nonetheless, in practical scenarios, CDRL faces many challenges due to the heterogeneity of agents and their learning tasks, different environments, time constraints of the learning, and resource limitations of wireless networks. To address these challenges, in this paper, a novel semantic-aware CDRL method is proposed to enable a group of heterogeneous untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network. To this end, a new heterogeneous federated DRL (HFDRL) algorithm is proposed to select the best subset of semantically relevant DRL agents for collaboration. The proposed approach then jointly optimizes the training loss and wireless bandwidth allocation for the cooperating selected agents in order to train each agent within the time limit of its real-time task. Simulation results show the superior performance of the proposed algorithm compared to state-of-the-art baselines.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 23 Nov 2021.

Builds on

16 of 25 references.

References · 16

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  12. 2017Collaborative Deep Reinforcement Learning for Joint Object Searchcited by 9
4 more
  1. 2018Integrated Millimeter Wave and Sub-6 GHz Wireless Networks: A Roadmap for Joint Mobile Broadband and Ultra-Reliable Low-Latency Communicationscited by 6
  2. 2021A Taxonomy of Similarity Metrics for Markov Decision Processescited by 6
  3. 2017Collaborative Deep Reinforcement Learningcited by 5
  4. 2021Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learningcited by 2

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Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 1.

6 papers.

  1. 2022Curriculum Learning for Goal-Oriented Semantic Communications with a Common Languagecited by 14
  2. 2022Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RANcited by 11
  3. 2022Semantics-Empowered Communication: A Tutorial-cum-Surveycited by 8
  4. 2024Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learningcited by 5
  5. 2023Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarioscited by 3
  6. 2023Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learningcited by 2