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DeLF: Designing Learning Environments with Foundation Models

Aida Afshar, Wenchao Li

TL;DR

The paper tackles the challenge of applying reinforcement learning in real-world tasks by addressing RL environment design, focusing on extracting good observation and action representations from user descriptions. It introduces DeLF, a method that leverages foundation models to design RL components and generate executable gym-like environment code through an Initiation-Communication-Evaluation (ICE) workflow. The authors formalize RL component design via a component extraction function, and demonstrate DeLF on four diverse tasks (Recommender System, Self-Driving Car, Swimmer, Key-Lock), producing runnable code after a small number of interactions. They also discuss extending to multimodal foundation models, refining evaluation metrics, and potential synergies with reward-design tools like Eureka, with all prompts and code made publicly available to encourage further development.

Abstract

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.

DeLF: Designing Learning Environments with Foundation Models

TL;DR

The paper tackles the challenge of applying reinforcement learning in real-world tasks by addressing RL environment design, focusing on extracting good observation and action representations from user descriptions. It introduces DeLF, a method that leverages foundation models to design RL components and generate executable gym-like environment code through an Initiation-Communication-Evaluation (ICE) workflow. The authors formalize RL component design via a component extraction function, and demonstrate DeLF on four diverse tasks (Recommender System, Self-Driving Car, Swimmer, Key-Lock), producing runnable code after a small number of interactions. They also discuss extending to multimodal foundation models, refining evaluation metrics, and potential synergies with reward-design tools like Eureka, with all prompts and code made publicly available to encourage further development.

Abstract

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.
Paper Structure (27 sections, 8 equations, 1 figure, 1 table)

This paper contains 27 sections, 8 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Prelimineries
  3. Foundation Models
  4. Reinforcement Learning
  5. Problem Setting
  6. Definitions
  7. The Problem of RL Component Design
  8. Language Models as RL Component Designers
  9. Method
  10. DeLF Initiation
  11. DeLF Communication
  12. DeLF Evaluation
  13. Experiments and Results
  14. Recommender System
  15. Self-Driving Car
  16. ...and 12 more sections

Figures (1)

  • Figure 1: Environment design with DeLF: The user provides a description of a learning scenario to the foundation model (e.g. large language models); the foundation model proposes a design for observation and action attributes; By having the basic template of the user's desirable RL API as context, DeLF is able to generate an initial sketch of the environment code that can be fed into an RL Algorithm.