# Interaction-Grounded Learning

arXiv: https://arxiv.org/abs/2106.04887 · HTML: https://mlsanity.com/paper/2106.04887/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Reinforcement Learning with Convex Constraints](https://mlsanity.com/paper/1906.09323/) (2019)
- [How agents see things: On visual representations in an emergent language game](https://mlsanity.com/paper/1808.10696/) (2018)
- [Latent Bandits Revisited](https://mlsanity.com/paper/2006.08714/) (2020)
- [An Information-Theoretic Approach to Minimax Regret in Partial Monitoring](https://mlsanity.com/paper/1902.00470/) (2019)
- [Latent Contextual Bandits and their Application to Personalized Recommendations for New Users](https://mlsanity.com/paper/1604.06743/) (2016)
- [Accelerated Robot Learning via Human Brain Signals](https://mlsanity.com/paper/1910.00682/) (2019)
- [Online Algorithm for Unsupervised Sensor Selection](https://mlsanity.com/paper/1901.04676/) (2019)
- [Online Algorithm for Unsupervised Sequential Selection with Contextual Information](https://mlsanity.com/paper/2010.12353/) (2020)

## Built on by (most cited first, 9 of 9)

- [Formalizing Learning from Language Feedback with Provable Guarantees](https://mlsanity.com/paper/2506.10341/) (2025)
- [Personalized Reward Learning with Interaction-Grounded Learning (IGL)](https://mlsanity.com/paper/2211.15823/) (2022)
- [A Rubric for Human-like Agents and NeuroAI](https://mlsanity.com/paper/2212.04401/) (2022)
- [Interaction-Grounded Learning with Action-inclusive Feedback](https://mlsanity.com/paper/2206.08364/) (2022)
- [Online Personalizing White-box LLMs Generation with Neural Bandits](https://mlsanity.com/paper/2404.16115/) (2024)
- [Learning to Guide and to Be Guided in the Architect-Builder Problem](https://mlsanity.com/paper/2112.07342/) (2021)
- [Provably Efficient Interactive-Grounded Learning with Personalized Reward](https://mlsanity.com/paper/2405.20677/) (2024)
- [An Information Theoretic Approach to Interaction-Grounded Learning](https://mlsanity.com/paper/2401.05015/) (2024)
- [Interaction-Grounded Learning for Contextual Markov Decision Processes with Personalized Feedback](https://mlsanity.com/paper/2602.08307/) (2026)

## Abstract (arXiv)

Consider a prosthetic arm, learning to adapt to its user's control signals. We propose Interaction-Grounded Learning for this novel setting, in which a learner's goal is to interact with the environment with no grounding or explicit reward to optimize its policies. Such a problem evades common RL solutions which require an explicit reward. The learning agent observes a multidimensional context vector, takes an action, and then observes a multidimensional feedback vector. This multidimensional feedback vector has no explicit reward information. In order to succeed, the algorithm must learn how to evaluate the feedback vector to discover a latent reward signal, with which it can ground its policies without supervision. We show that in an Interaction-Grounded Learning setting, with certain natural assumptions, a learner can discover the latent reward and ground its policy for successful interaction. We provide theoretical guarantees and a proof-of-concept empirical evaluation to demonstrate the effectiveness of our proposed approach.