June 5, 2024, 4:43 a.m. | Nadav Merlis

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02258v1 Announce Type: new
Abstract: We study reinforcement learning (RL) problems in which agents observe the reward or transition realizations at their current state before deciding which action to take. Such observations are available in many applications, including transactions, navigation and more. When the environment is known, previous work shows that this lookahead information can drastically increase the collected reward. However, outside of specific applications, existing approaches for interacting with unknown environments are not well-adapted to these observations. In this …

abstract action agents applications arxiv cs.lg current environment information navigation observe reinforcement reinforcement learning shows state stat.ml study the environment transactions transition type work

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