June 6, 2024, 4:42 a.m. | Aidan Scannell, Kalle Kujanp\"a\"a, Yi Zhao, Mohammadreza Nakhaei, Arno Solin, Joni Pajarinen

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02696v1 Announce Type: new
Abstract: Learning representations for reinforcement learning (RL) has shown much promise for continuous control. We propose an efficient representation learning method using only a self-supervised latent-state consistency loss. Our approach employs an encoder and a dynamics model to map observations to latent states and predict future latent states, respectively. We achieve high performance and prevent representation collapse by quantizing the latent representation such that the rank of the representation is empirically preserved. Our method, named iQRL: …

abstract arxiv continuous control cs.lg dynamics encoder future loss map reinforcement reinforcement learning representation representation learning sample state type

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