Reward tampering problems and solutions in reinforcement learning: a causal influence diagram perspective.
Can humans get arbitrarily capable reinforcement learning (RL) agents to do their bidding? Or will sufficiently capable RL agents always find ways to bypass their intended objectives by shortcutting their reward signal? This question impacts how far RL can be scaled, and whether alternative paradigm...
| Publicado en: | Synthese Vol. 198; no. 27; pp. 6435 - 6468 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Nov2021 Supplement 27
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |