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cs.LG2022
Temporal Alignment for History Representation in Reinforcement Learning
Aleksandr Ermolov, Enver Sangineto, Nicu Sebe
Environments in Reinforcement Learning are usually only partially observable. To address this problem, a possible solution is to provide the agent with information about the past.…
cs.LG2020★ 13 cited
Latent World Models For Intrinsically Motivated Exploration
Aleksandr Ermolov, Nicu Sebe
In this work we consider partially observable environments with sparse rewards. We present a self-supervised representation learning method for image-based observations, which arra…