New research targets ethical RL agents by focusing on per-episode violations

A new research paper proposes training reinforcement learning agents to exhibit ethical behavior by focusing on per-episode distributions rather than average performance. The study compares four training methods within the Craftax benchmark, finding that an approach optimizing a non-compensatory utility per episode under the Expected Scalarized Returns (ESR) criterion is most effective. This ESR method ensures a stated budget of violations is met in nearly every episode, unlike other methods that allow violations to average out or exceed the budget in worst-case scenarios. AI

IMPACT Proposes a new training paradigm for AI agents to ensure ethical behavior consistently, rather than on average.

RANK_REASON Research paper detailing a novel method for training ethical AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• Craftax
• DagsHub
• ESR
• Expected Scalarized Returns
• Gotit.pub
• Hugging Face
• IArxiv
• Influence Flower
• Lagrange function
• reinforcement learning
• ScienceCast

• paper
• safety

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