On Learning by Exchanging Advice

Title: On Learning by Exchanging Advice

Abstract: This research investigated how agents can benefit from exchanging advice during the learning process. The study used a simplified traffic-control simulation and four different learning techniques: Random Walk, Simulated Annealing, Evolutionary Algorithms, and Q-Learning. The main finding was that advice-exchange can improve learning speed, although the use of bad advice or blind reliance could hinder learning performance. The authors suggested that supervised learning, which incorporates advice from non-expert peers using different learning algorithms, could be a promising technique in Multi-Agent Systems where no supervision information is available.

Research Question: How can agents benefit from mutual interaction during the learning process?

Methodology: The researchers developed an interactive advice-exchange mechanism to enhance the performance of Learning Agents facing similar problems in an environment with limited reinforcement information. They applied four different learning techniques and compared the agents' performance with and without advice exchange.

Results: The results indicated that advice-exchange could improve learning speed. However, the use of bad advice or blind reliance could negatively impact learning performance.

Implications: The research suggests that advice-exchange can be a beneficial technique for improving learning efficiency in Multi-Agent Systems. It also highlights the importance of regulating the exchange of information to prevent the use of bad advice and the reliance on a single source of information.

Link to Article: https://arxiv.org/abs/0203010v1 Authors: arXiv ID: 0203010v1