The paper introduces a scalable method for maximizing empowerment, defined as the number of executable skills an agent can perform from a given state. By linking skill learning to empowerment, the authors provide a recipe that works with any underlying skill learning approach, enabling large‑scale studies beyond traditional tabular settings. Experiments demonstrate that maximizing empowerment leads to emergent behaviors such as resource gathering, environment modification, and even building a house in the game Crafter.
By Catherine Ji, Vivek Myers, Joshua Lin, Alexandra Wingate, Sergey Levine, Benjamin Eysenbach
arXiv:2604.21155v2 Announce Type: replace
Abstract: Intrinsic motivations are receiving increasing attention, i.e. behavioral incentives that are not engineered, but emerge from the interaction of an...
By Tristan Shah, Ilya Nemenman, Daniel Polani, Stas Tiomkin
The paper introduces a Bellman optimality equation for optimizing plasticity in Markov decision processes, extending recent work that reframes the stability‑plasticity dilemma as an empowerment‑plasticity tradeoff. It builds on Abel et al. (2025), which defined plasticity as the generalized directed information from observations to actions and empowerment as the reverse. This work is the first to address plasticity optimization under that new definition, providing a theoretical foundation similar to existing empowerment‑based approaches.
By Jeremy Lucas, Doina Precup
arXiv:2511. 04177v2 Announce Type: replace Abstract: Personal AI agents are increasingly deployed in shared environments, where their actions affect not just the primary user they are assisting, but bystanders who never consented to being affected by the system.
By Claire Yang, Claire Jie Zhang, Maya Cakmak, Max Kleiman-Weiner
The paper proposes a developmental framework for autonomous artificial agents that emphasizes learning social norms and alignment through direct interaction with dynamic environments. It argues that intrinsic motivations such as curiosity and competence can guide exploration, but also complicate alignment with human goals. By drawing parallels to child development, the authors suggest that regulatory sandboxes serve as pedagogical spaces where agents gradually acquire moral agency and adapt their behaviors through experience and cooperation.
By Marica Notte, Ludovica Marinucci, Vieri Giuliano Santucci
arXiv:2608. 08240v1 Announce Type: new Abstract: This paper explores the idea of promoting well-being and safety in human-AI interactions by forcing AI agents explicitly to empower humans and to manage the power balance between humans and AI agents in a desirable way.
By Jobst Heitzig, Ram Potham