# Agency Is Frame-Dependent

arXiv: https://arxiv.org/abs/2502.04403 · HTML: https://mlsanity.com/paper/2502.04403/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 6 of 6)

- [A Definition of Continual Reinforcement Learning](https://mlsanity.com/paper/2307.11046/) (2023)
- [Identifiability in inverse reinforcement learning](https://mlsanity.com/paper/2106.03498/) (2021)
- [Occam's razor is insufficient to infer the preferences of irrational agents](https://mlsanity.com/paper/1712.05812/) (2017)
- [Repeated Inverse Reinforcement Learning](https://mlsanity.com/paper/1705.05427/) (2017)
- [Discovering Agents](https://mlsanity.com/paper/2208.08345/) (2022)
- [On Value Functions and the Agent-Environment Boundary](https://mlsanity.com/paper/1905.13341/) (2019)

## Built on by (most cited first, 5 of 5)

- [Agentic AI Needs a Systems Theory](https://mlsanity.com/paper/2503.00237/) (2025)
- [A Quantifiable Information-Processing Hierarchy Provides a Necessary Condition for Detecting Agency](https://mlsanity.com/paper/2601.03498/) (2026)
- [Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary](https://mlsanity.com/paper/2603.06813/) (2026)
- [Distributions as Actions: A Unified Framework for Diverse Action Spaces](https://mlsanity.com/paper/2506.16608/) (2025)
- [Causal Foundations of Collective Agency](https://mlsanity.com/paper/2605.00248/) (2026)

## Abstract (arXiv)

Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.