# DeLF: Designing Learning Environments with Foundation Models

arXiv: https://arxiv.org/abs/2401.08936 · HTML: https://mlsanity.com/paper/2401.08936/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 17 Jan 2024.

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Language to Rewards for Robotic Skill Synthesis](https://mlsanity.com/paper/2306.08647/) (2023)
- [Bridging RL Theory and Practice with the Effective Horizon](https://mlsanity.com/paper/2304.09853/) (2023)
- [PyFlyt -- UAV Simulation Environments for Reinforcement Learning Research](https://mlsanity.com/paper/2304.01305/) (2023)

## Built on by (most cited first, 2 of 2)

- [EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents](https://mlsanity.com/paper/2403.12014/) (2024)
- [A Research Agenda for Usability and Generalisation in Reinforcement Learning](https://mlsanity.com/paper/2412.16970/) (2024)

## Abstract (arXiv)

Reinforcement learning (RL) offers a capable and intuitive structure for the fundamental sequential decision-making problem. Despite impressive breakthroughs, it can still be difficult to employ RL in practice in many simple applications. In this paper, we try to address this issue by introducing a method for designing the components of the RL environment for a given, user-intended application. We provide an initial formalization for the problem of RL component design, that concentrates on designing a good representation for observation and action space. We propose a method named DeLF: Designing Learning Environments with Foundation Models, that employs large language models to design and codify the user's intended learning scenario. By testing our method on four different learning environments, we demonstrate that DeLF can obtain executable environment codes for the corresponding RL problems.