How can I reduce overestimation in multi-agent Q-learning?
Dual Ensembled Multiagent Q-Learning with Hypernet Regularizer
This paper addresses the problem of overestimation in multi-agent reinforcement learning (MARL), where estimated values are consistently higher than true values, leading to unstable learning. It proposes a new algorithm called DEMAR (Dual Ensembled MultiAgent Q-learning with hypernet Regularizer) that reduces overestimation through two main mechanisms: using ensembles of Q-networks to create lower update targets and applying a hypernet regularizer to constrain network optimization.
Key points for LLM-based multi-agent systems: DEMAR's focus on improving learning stability is crucial for LLM agents, which are known to be sensitive to training instability. The use of ensembles and regularization techniques could translate well into mitigating overestimation issues that might arise in LLM-based agents during training and interaction. While not directly addressed, the paper's analysis of overestimation's impact on network optimization is relevant for LLM agents' complex neural architectures. Furthermore, DEMAR's successful application in cooperative MARL settings suggests potential benefits for developing collaborative LLM agents.