Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni, Lijun Chen
The paper presents a unified framework for addressing locality in scalable multi-agent reinforcement learning (MARL) by introducing a policy-dependent approach to the exponential decay property (EDP) of value functions.
In multi-agent reinforcement learning, handling the complexity of many interacting agents is a big challenge. The authors propose a new way to simplify this by focusing on how the policies of the agents themselves can affect interactions, rather than just the environment. They show that even if the environment is complex, a well-designed policy can help manage this complexity. This helps in developing more efficient methods to improve agent policies and ensure that the learning process remains manageable.
