Allowance
发表于 2025-3-28 15:26:25
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OTHER
发表于 2025-3-28 22:06:36
Model-Based Offline Policy Optimization with Distribution Correcting Regularizationon and offline data distribution via the DICE framework [.], and then regularizes the model-predicted rewards with the ratio for pessimistic policy learning. Extensive experiments show our DROP can achieve comparable or better performance compared to baselines on widely studied offline RL benchmarks.
毕业典礼
发表于 2025-3-28 23:05:46
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humectant
发表于 2025-3-29 05:41:21
Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learningnment while periodically sharing knowledge amongst policies in the ensemble through knowledge distillation. Our experiments demonstrate that PIEKD improves upon a state-of-the-art RL method in sample efficiency on several challenging MuJoCo benchmark tasks. Additionally, we perform ablation studies to better understand PIEKD.
遍及
发表于 2025-3-29 08:34:51
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learningver, we derive a refined bias-variance-covariance decomposition to analyze the different ways of learning ensembles and using auxiliary tasks, and use the analysis to help provide some understanding of the case study. Our code is open source and available at ..
从属
发表于 2025-3-29 15:17:48
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buoyant
发表于 2025-3-29 19:06:50
Conservative Online Convex Optimizationgret algorithm for online convex optimization into one that, at the same time, satisfies the conservativeness constraint and maintains the same regret order. Finally, we run an extensive experimental campaign, comparing and analyzing the performance of our meta-algorithm with that of state-of-the-art algorithms.
Binge-Drinking
发表于 2025-3-29 23:25:05
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FLAG
发表于 2025-3-30 03:26:15
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BILL
发表于 2025-3-30 04:07:20
978-3-030-86485-9Springer Nature Switzerland AG 2021