用语言模型生成自补全指令，提升强化学习性能。
elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings
- 将语言模型与自补全指令框架结合，实现文本驱动的RL任务设定。
- 实验证明语言指令可有效提升强化学习代理表现。
- 开源工具支持快速部署，适合研究者快速验证语言方案。
我们提出 elsciRL，一个开源 Python 库，用于在强化学习问题中应用语言解决方案。通过结合 (Osborne, 2024) 提出的 Language Adapter with Self-Completing Instruction 框架与大语言模型（LLM），展示了该方法的潜力。该方法具备低配置要求，可轻松迁移至新场景。我们还提供了一个新颖的图形界面，允许用户输入文本，由 LLM 生成并自动补全指令。实证结果表明，这些语言指令确实能提升强化学习代理的性能。本工作旨在加速语言解决方案在基于奖励环境中的评估，推动科学发现的新可能。
原文摘要 · Abstract (English)
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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