elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings
This work provides a tool to accelerate the evaluation of language solutions in reward-based environments, though it is incremental as it builds on existing frameworks.
The authors introduced elsciRL, an open-source Python library that integrates language solutions into reinforcement learning problems, demonstrating that instructions generated by LLMs can improve an agent's performance.
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.