How can agents explore cooperatively and efficiently?
AIR: Unifying Individual and Cooperative Exploration in Collective Multi-Agent Reinforcement Learning
This paper introduces AIR (Adaptive exploration via Identity Recognition), a new method for improving exploration in cooperative multi-agent reinforcement learning (MARL) using value-based agents. AIR addresses the limitations of existing exploration strategies by unifying individual exploration (agents acting independently) and collective exploration (agents coordinating actions). It uses an "identity classifier" to distinguish between agents based on their actions and encourages diverse behaviors.
For LLM-based multi-agent systems, AIR offers a potential solution for enhancing agent exploration and coordination by dynamically adjusting between individual and collective exploration strategies throughout the training process. This adaptable approach is especially relevant when dealing with complex tasks requiring varied skills and cooperation, potentially improving the efficiency and effectiveness of LLM agents in collaborative environments.