Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Nuria Oliver,Fernando Pérez-Cruz,Jose A. Lozano

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OTHER

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

humectant

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

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

buoyant

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

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BILL

978-3-030-86485-9Springer Nature Switzerland AG 2021