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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…
cs.LG2021★ 1 cited
Adapting to Reward Progressivity via Spectral Reinforcement Learning
Michael Dann, John Thangarajah
In this paper we consider reinforcement learning tasks with progressive rewards; that is, tasks where the rewards tend to increase in magnitude over time. We hypothesise that this…