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environment sampling

topic1 events
papersTODAY 04:00 UTC

arXiv Paper Proposes Specifying RL Reward Functions Without Environment Sampling

A new arXiv preprint describes a method for letting stakeholders define reward functions for reinforcement learning agents without needing to sample from the environment. The authors position the work as reducing the manual effort of reward design that preference-based approaches like online RLHF are meant to address. The same paper was listed in both the cs.AI and cs.LG announcement feeds.

reinforcement-learning/rlhf/reward-design/arxiv