Beyond state, random noise, and constant vectors, several other types of centralized information could be explored to enhance the performance of factorization algorithms in multi-agent reinforcement learning. One promising avenue is the use of historical performance metrics, such as the average return or success rate of previous episodes. By incorporating this information, agents can adjust their strategies based on past experiences, potentially leading to more informed decision-making.
Another type of centralized information that could be beneficial is the use of agent-specific contextual information, such as the roles or capabilities of each agent within the team. This could help tailor the learning process to the strengths and weaknesses of individual agents, allowing for more specialized utility estimators that reflect the unique contributions of each agent to the overall task.
Furthermore, incorporating environmental features or dynamics, such as obstacles or resource availability, as centralized information could provide agents with a more comprehensive understanding of their surroundings. This could enhance their ability to coordinate and collaborate effectively, especially in complex environments where spatial awareness is crucial.
Lastly, exploring the integration of external knowledge sources, such as expert demonstrations or domain-specific heuristics, could provide agents with additional guidance during the learning process. This could be particularly useful in scenarios where agents face sparse rewards or long time horizons, as it may help accelerate learning and improve overall performance.