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most citedAdapting to Reward Progressivity via Spectral Reinforcement Learning

1 citations · 2 across the 3 of their papers we have counts for

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cs.LG2022

SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning

Andrew Chester, Michael Dann, Fabio Zambetta +1

Model-based reinforcement learning algorithms are typically more sample efficient than their model-free counterparts, especially in sparse reward problems. Unfortunately, many inte…