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COMBO: Conservative Offline Model-Based Policy Optimization
[article]
2022
arXiv
pre-print
Model-based algorithms, which learn a dynamics model from logged experience and perform some sort of pessimistic planning under the learned model, have emerged as a promising paradigm for offline reinforcement learning (offline RL). However, practical variants of such model-based algorithms rely on explicit uncertainty quantification for incorporating pessimism. Uncertainty estimation with complex models, such as deep neural networks, can be difficult and unreliable. We overcome this limitation
arXiv:2102.08363v2
fatcat:azvca4wb65gc5aypjpidqphgzi