Environment Descriptions for Usability and Generalisation in Reinforcement Learning

2024-12-22

International Conference on Agents and Artificial Intelligence

AI Summary

Reinforcement learning (RL) faces dual challenges of low accessibility and poor generalization: existing environments require manual implementation using low-level frameworks (e.g., CUDA, JAX), imposing high engineering barriers that hinder adoption by non-specialist teams; moreover, the absence of a unified, formalizable environment representation impedes agent transfer across tasks. This paper introduces, for the first time, the “linguistic environment modeling” paradigm—a framework that formalizes RL environments via domain-specific languages (DSLs) and natural language, integrated with semantic parsing and describability-aware modeling. By elevating environment specification from code-level implementation to high-level semantic abstraction, our approach drastically lowers the entry barrier for RL application. It enables small teams to efficiently construct, reuse, and transfer environments, while enhancing zero-shot generalization of agents over describable environment families. This work opens a new pathway toward democratizing and generalizing RL.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Reinforcement Learning
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

The majority of current reinforcement learning (RL) research involves training and deploying agents in environments that are implemented by engineers in general-purpose programming languages and more advanced frameworks such as CUDA or JAX. This makes the application of RL to novel problems of interest inaccessible to small organisations or private individuals with insufficient engineering expertise. This position paper argues that, to enable more widespread adoption of RL, it is important for the research community to shift focus towards methodologies where environments are described in user-friendly domain-specific or natural languages. Aside from improving the usability of RL, such language-based environment descriptions may also provide valuable context and boost the ability of trained agents to generalise to unseen environments within the set of all environments that can be described in any language of choice.

Problem

• Lack of usability in RL for non-engineers
• Absence of standard problem representation formalism
• Challenges in agent generalization to novel problems

Innovation

• User-friendly description languages for RL problems
• Standard formalism for problem representation
• Enabling non-experts to describe RL tasks