How to value information in delayed action planning?
INCREASING THE VALUE OF INFORMATION DURING PLANNING IN UNCERTAIN ENVIRONMENTS
This research tackles the challenge of making online planning algorithms, specifically POMCP, more effective in situations where gathering information is crucial but delayed rewards make it difficult for the algorithm to recognize its value.
A key insight relevant to LLM-based multi-agent systems is the proposed solution: incorporating "entropy" into the decision-making process. By favoring actions that reduce uncertainty (i.e., lower entropy), even if they don't yield immediate rewards, the algorithm becomes better at recognizing the long-term value of information gathering. This is particularly relevant for LLM-based agents which often operate in information-rich environments where strategically acquiring information is key.