# Representation and Invariance in Reinforcement Learning

arXiv: https://arxiv.org/abs/2112.07752 · HTML: https://mlsanity.com/paper/2112.07752/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI](https://mlsanity.com/paper/2002.10221/) (2020)
- [Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents](https://mlsanity.com/paper/1910.09721/) (2019)
- [Measuring Intelligence and Growth Rate: Variations on Hibbard's Intelligence Measure](https://mlsanity.com/paper/2101.12047/) (2021)
- [Reward-Punishment Symmetric Universal Intelligence](https://mlsanity.com/paper/2110.02450/) (2021)
- [Extending Environments To Measure Self-Reflection In Reinforcement Learning](https://mlsanity.com/paper/2110.06890/) (2021)

## Abstract (arXiv)

Researchers have formalized reinforcement learning (RL) in different ways. If an agent in one RL framework is to run within another RL framework's environments, the agent must first be converted, or mapped, into that other framework. In this paper, we lay foundations for studying relative-intelligence-preserving mappability between RL frameworks. We introduce a criterion which is sufficient for relative intelligence to be preserved according to one particular method of measuring intelligence. We show that this criterion cannot be met when mapping between certain deterministic and stochastic RL frameworks, suggesting inherent fundamental differences between these different versions of RL.