| 1 |
Rethinking Decision Transformer via Hierarchical Reinforcement Learning |
通过层次强化学习重新思考决策变换器 |
reinforcement learning offline RL decision transformer |
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| 2 |
Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning |
提出深度强化学习方法以优化顺序反事实解释策略 |
reinforcement learning deep reinforcement learning |
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| 3 |
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards |
提出优先级与多样性策略以提升稀疏奖励下自我模仿强化学习的泛化能力 |
reinforcement learning imitation learning |
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| 4 |
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning |
提出选择性经验共享方法以提升多智能体强化学习效果 |
reinforcement learning |
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| 5 |
SCPO: Safe Reinforcement Learning with Safety Critic Policy Optimization |
提出SCPO以解决强化学习中的安全性问题 |
reinforcement learning |
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| 6 |
REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning |
提出REBAR方法以解决时间序列对比学习中的正样本构建问题 |
contrastive learning |
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| 7 |
NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks |
提出NEO-KD以解决多出口神经网络的对抗攻击问题 |
distillation |
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| 8 |
Multi-task Representation Learning for Pure Exploration in Bilinear Bandits |
提出GOBLIN算法以解决双线性赌博机中的纯探索问题 |
representation learning |
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| 9 |
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning |
提出联邦自然策略梯度与演员-评论家方法以解决多任务强化学习问题 |
reinforcement learning |
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