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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
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…