# Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning

arXiv: https://arxiv.org/abs/2001.04418 · HTML: https://mlsanity.com/paper/2001.04418/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Learning Invariable Semantical Representation from Language for Extensible Policy Generalization](https://mlsanity.com/paper/2202.00466/) (2022)

## Abstract (arXiv)

In this work, we present an alternative approach to making an agent compositional through the use of a diagnostic classifier. Because of the need for explainable agents in automated decision processes, we attempt to interpret the latent space from an RL agent to identify its current objective in a complex language instruction. Results show that the classification process causes changes in the hidden states which makes them more easily interpretable, but also causes a shift in zero-shot performance to novel instructions. Lastly, we limit the supervisory signal on the classification, and observe a similar but less notable effect.