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reward function

topic2 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
papersSEP 11 04:00 UTC

Statistical Analysis of Inverse Entropy-Regularized Reinforcement Learning

A new arXiv paper examines inverse reinforcement learning, where the goal is to recover the reward function that best explains an expert's observed state-action trajectories. The authors focus on the long-standing problem that classical IRL methods can return multiple, non-unique reward functions, and provide a statistical treatment of an entropy-regularized formulation.

reinforcement-learning/inverse-rl/research/arxiv