elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings

Philip Osborne; Danilo S. Carvalho; André Freitas

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arXiv · 2507.08705

elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings

Philip Osborne·Danilo S. Carvalho·André Freitas

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Abstract

We present elsciRL, an open-source Python library to facilitate the application of language solutions on reinforcement learning problems. We demonstrate the potential of our software by extending the Language Adapter with Self-Completing Instruction framework defined in (Osborne, 2024) with the use of LLMs. Our approach can be re-applied to new applications with minimal setup requirements. We provide a novel GUI that allows a user to provide text input for an LLM to generate instructions which it can then self-complete. Empirical results indicate that these instructions \textit{can} improve a reinforcement learning agent's performance. Therefore, we present this work to accelerate the evaluation of language solutions on reward based environments to enable new opportunities for scientific discovery.

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BibTeX RIS

Philip Osborne, Danilo S. Carvalho, André Freitas. 2025-07-11. elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings. https://arxiv.org/abs/2507.08705

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