Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks | Paper Citation Record

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This paper cites Deep reinforcement learning for autonomous driving: A survey,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Deep reinforcement learning for autonomous driving: A survey,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Rein- forcement learning for mobile robotics exploration: A survey,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Reinforcement learning based recommender systems: A survey,

Reference 3

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This paper cites Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Reference 4

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Multi-agent deep reinforcement learning-based task scheduling and resource sharing for o-ran-empowered multi-uav-assisted wireless sensor networks,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Toward autonomous multi-uav wireless network: A survey of reinforcement learning-based approaches,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Applications of deep reinforcement learning in communications and networking: A survey,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Transformers in Reinforcement Learning: A Survey

Reference 8

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This paper cites TransDreamer: Reinforcement Learning with Transformer World Models.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks TransDreamer: Reinforcement Learning with Transformer World Models

Reference 9

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This paper cites Deep Transformer Q-Networks for Partially Observable Reinforcement Learning.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Deep Transformer Q-Networks for Partially Observable Reinforcement Learning

Reference 10

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Attention is all you need,

Reference 11

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This paper cites Autonomous link control in digital twin aided mobile network: From virtual channel generation to intelligent power allocation,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Autonomous link control in digital twin aided mobile network: From virtual channel generation to intelligent power allocation,

Reference 12

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This paper cites Dsaf-former: Drl based sub-channel assignment framework using transformer in mmwave iabn,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Dsaf-former: Drl based sub-channel assignment framework using transformer in mmwave iabn,

Reference 13

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Tpto: A transformer-ppo based task offloading solution for edge computing environments,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Transformer- based distributed task offloading and resource management in cloud- edge computing networks,

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This paper cites From perception to action: Transformer-enhanced deep reinforcement learning for autonomous robot navigation,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks From perception to action: Transformer-enhanced deep reinforcement learning for autonomous robot navigation,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Transformer based collaborative reinforcement learning for fluid antenna system (fas)-enabled 3d uav positioning,

Reference 17

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This paper cites Anti-jamming task schedul- ing in mec-o-ran with hierarchical drl and transformer-based control,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Anti-jamming task schedul- ing in mec-o-ran with hierarchical drl and transformer-based control,

Reference 18

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Radar: Robust drl-based resource allocation against adversarial attacks in intelligent o-ran,

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This paper cites Enhancing iot intelligence: A transformer-based reinforcement learn- ing methodology,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Enhancing iot intelligence: A transformer-based reinforcement learn- ing methodology,

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This paper cites A comparison of neural networks for wireless channel prediction,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks A comparison of neural networks for wireless channel prediction,

Reference 21

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Machine Learning for Future Wireless Communications: Channel Prediction Perspectives

Reference 22

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This paper cites Generative ai for deep reinforcement learning: Framework, analysis, and use cases,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Generative ai for deep reinforcement learning: Framework, analysis, and use cases,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Dueling network architectures for deep reinforcement learning,

Reference 24

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks On transforming reinforcement learning with transformers: The development trajectory,

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This paper cites Mastering atari, go, chess and shogi by planning with a learned model,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Mastering atari, go, chess and shogi by planning with a learned model,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Mastering Atari with Discrete World Models

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This paper cites Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark

Reference 28

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks A Survey on Transformers in Reinforcement Learning

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Continuous control with deep reinforcement learning

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Unresolved cited work

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Diaformer: Automatic diagnosis via symptoms sequence generation,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Addressing optimism bias in sequence modeling for reinforcement learning,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Deep residual learning for image recognition,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Layer Normalization

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Deep Learning using Rectified Linear Units (ReLU)

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Gaussian error linear units (gelus),

Reference 39

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks A survey of transformers,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Generating Long Sequences with Sparse Transformers

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Transformers are rnns: Fast autoregressive transformers with linear attention,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Rethinking attention with performers,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Linear transformers are secretly fast weight programmers,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Generating wikipedia by summarizing long sequences,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Fast transformers with clustered attention,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Poolingformer: Long document modeling with pooling attention,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Compressed self-attention for deep metric learning with low-rank approximation,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Nyströmformer: A nyström-based algorithm for approximat- ing self-attention,

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This paper cites Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Exploring the limits of transfer learning with a unified text-to-text transformer,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Rethinking positional encoding in language pre-training,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Modeling localness for self-attention networks,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Multi-head attention with disagreement regularization,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Revealing the dark secrets of bert,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Adaptive attention span in transformers,

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This paper cites Multi-scale self- attention for text classification,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Multi-scale self- attention for text classification,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Information aggregation for multi-head attention with routing-by-agreement,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Improving multi-head attention with capsule networks,

Reference 59

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks An image is worth16×16words: Transformers for image recognition at scale,

Reference 60

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 61

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks End-to-end object detection with transformers,

Reference 62

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Segment anything,

Reference 63

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Graph Attention Networks,

Reference 64

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Graph transformer networks,

Reference 65

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Heterogeneous graph trans- former,

Reference 66

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Do transformers really perform badly for graph representation?

Reference 67

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Long short-term memory,

Reference 68

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This paper cites Multimodal Learning With Transformers: A Survey ,.

Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Multimodal Learning With Transformers: A Survey ,

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Multi-Game Decision Transformers

Reference 70

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Q-learning,

Reference 71

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Playing Atari with Deep Reinforcement Learning

Reference 72

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Proximal Policy Optimization Algorithms

Reference 73

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Monotonic value function factorisation for deep multi- agent reinforcement learning,

Reference 74

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems

Reference 75

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks A transformer-based thermal surrogate model for cooling control in data centers,

Reference 76

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Trandrl: A transformer-driven deep reinforcement learning enabled prescriptive maintenance framework,

Reference 77

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks A deep reinforcement learning with transformer integration for directed acyclic graph scheduling in edge networks,

Reference 78

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Robust downlink data transmission in leo satellite-terrestrial networks: A rate- splitting multiple access approach,

Reference 79

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Learning-based task-centric multi-user semantic communication solu- tion for vehicle networks,

Reference 80

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Decision transformer: Rein- forcement learning via sequence modeling,

Reference 81

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Compressive Transformers for Long-Range Sequence Modelling

Reference 82

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks CoBERL: Contrastive BERT for Reinforcement Learning

Reference 83

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks You can’t count on luck: Why decision transformers and rvs fail in stochastic environments,

Reference 84

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Q-learning deci- sion transformer: Leveraging dynamic programming for conditional sequence modelling in offline rl,

Reference 85

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Offline reinforcement learning as one big sequence modeling problem,

Reference 86

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Bandwidth reservation for time-critical vehicular applications: A multi-operator environment,

Reference 87

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Transformer- based packet scheduling under strict delay and buffer constraints,

Reference 88

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Mgco: Mobility-aware generative computation offloading in edge-cloud sys- tems

Reference 89

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Handover-free multi-connectivity mobility management for downlink fd-ran: A hierarchical drl based approach,

Reference 90

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Hybrid model-aided learning for 5g-ntn handover in high-mobility platforms,

Reference 91

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks A transformer-embedded reinforce- ment learning for computing power scheduling in data centers,

Reference 92

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Application of a ppo-based scheduling algorithm with multi-dimensional attention mechanisms in satellite edge computing,

Reference 93

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Agentformer: Agent- aware transformers for socio-temporal multi-agent forecasting,

Reference 94

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Cooperative dnn partitioning in energy-harvesting and mec-enabled uav networks,

Reference 95

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Enhanced reinforcement learning based multi-node cooperative deployment strat- egy for uav monitoring,

Reference 96

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks A hierarchical conflict resolution framework with graph transformer-based reinforcement learning for heterogeneous uav networks,

Reference 97

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Distributed generative reinforce- ment learning for stable service function chain orchestration in highly dynamic uav swarm networks,

Reference 98

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Attention-enhanced prompt decision transformers for aav-assisted communications with aoi,

Reference 99

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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks Efficient reductions for imitation learning,

Reference 100

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