Co-Learning: Code Learning for Multi-Agent Reinforcement Collaborative Framework with Conversational Natural Language Interfaces

TL;DR

This paper introduces Co-Learning, a multi-agent reinforcement learning framework utilizing conversational natural language interfaces to improve code error correction accuracy and efficiency in LLM-based Q&A systems.

Contribution

It proposes a novel multi-agent environment with environmentally reinforcement learning for code correction, enhancing accuracy and reducing correction time.

Findings

3% improvement in Precision score

15% reduction in correction time

Effective multi-agent collaboration for code error correction

Abstract

Online question-and-answer (Q\&A) systems based on the Large Language Model (LLM) have progressively diverged from recreational to professional use. This paper proposed a Multi-Agent framework with environmentally reinforcement learning (E-RL) for code correction called Code Learning (Co-Learning) community, assisting beginners to correct code errors independently. It evaluates the performance of multiple LLMs from an original dataset with 702 error codes, uses it as a reward or punishment criterion for E-RL; Analyzes input error codes by the current agent; selects the appropriate LLM-based agent to achieve optimal error correction accuracy and reduce correction time. Experiment results showed that 3\% improvement in Precision score and 15\% improvement in time cost as compared with no E-RL method respectively. Our source code is available at: https://github.com/yuqian2003/Co_Learning