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Model-Based Offline Meta-Reinforcement Learning with Regularization
[article]
2022
arXiv
pre-print
Existing offline reinforcement learning (RL) methods face a few major challenges, particularly the distributional shift between the learned policy and the behavior policy. Offline Meta-RL is emerging as a promising approach to address these challenges, aiming to learn an informative meta-policy from a collection of tasks. Nevertheless, as shown in our empirical studies, offline Meta-RL could be outperformed by offline single-task RL methods on tasks with good quality of datasets, indicating
arXiv:2202.02929v2
fatcat:x7ygjoq46vhrlf7sbpdxfp37my