| 1 |
Model-based deep reinforcement learning for accelerated learning from flow simulations |
提出基于模型的深度强化学习以加速流动仿真学习 |
reinforcement learning deep reinforcement learning |
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| 2 |
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning |
提出Craftax以解决开放式强化学习基准测试效率低下问题 |
reinforcement learning PPO |
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| 3 |
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory |
提出C-GAIL以解决GAIL训练不稳定问题 |
reinforcement learning imitation learning |
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| 4 |
Graph Diffusion Policy Optimization |
提出图扩散策略优化以解决图生成中的优化问题 |
reinforcement learning diffusion policy |
✅ |
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| 5 |
m2mKD: Module-to-Module Knowledge Distillation for Modular Transformers |
提出模块间知识蒸馏方法以提升模块化变换器性能 |
distillation |
✅ |
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| 6 |
Hyperdimensional Representation Learning for Node Classification and Link Prediction |
提出超高维图学习方法HDGL以解决节点分类和链接预测问题 |
representation learning |
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| 7 |
Program-Based Strategy Induction for Reinforcement Learning |
提出基于程序的策略诱导以解决强化学习中的策略发现问题 |
reinforcement learning |
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| 8 |
QF-tuner: Breaking Tradition in Reinforcement Learning |
提出QF-tuner以解决强化学习中的超参数调优问题 |
reinforcement learning |
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| 9 |
Minimize Control Inputs for Strong Structural Controllability Using Reinforcement Learning with Graph Neural Network |
提出基于图神经网络的强化学习方法以最小化控制输入 |
reinforcement learning |
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| 10 |
Multiple Access in the Era of Distributed Computing and Edge Intelligence |
提出下一代多接入技术以应对6G网络挑战 |
reinforcement learning distillation |
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