A location-based advising method in teacher–student frameworks

Abstract

The integration of a teacher–student framework in multi-agent learning has proven to be an effective approach for improving learning performance. This framework facilitates the exchange of advice among agents, enabling them to learn from each other. However, contemporary advising methods face a common limitation in which teacher agents can only provide poor advice to student agents when the latter are in states that contain limited environmental information. These states are referred to as ”cold states” and are prevalent in multi-agent environments. To overcome this challenge, we propose a novel location-based advising method. This approach utilizes the student agent’s location as well as their current state to generate advice from the teacher agent. By doing so, we can avoid the cold-state issue altogether. Our experimental results demonstrate that this method significantly improves the learning performance of agents when compared to state-of-the-art advising methods. In summary, our proposed location-based advising method addresses the limitations of contemporary advising methods in multi-agent learning, specifically the cold-state issue. Our approach utilizes both the student’s location and state information to generate advice from the teacher, leading to improved learning performance.