Recursive Strategy Refinement Algorithms in Multi-Agent Collaborative Environments

Abstract

A multi-agent collaboration scenario necessitates coordination, effective task assignment, and strategic adjustment in order to attain maximum efficiency in a multi-agent environment. Existing solutions such as fixed heuristics and reinforcement learning often fail in scalability, convergence speed, and adaptability when employed in dynamic, diverse multi-agent systems. In this research, a Recursive Strategy Refinement model is suggested in which the agent's strategy can be adjusted recursively depending on feedback from their previous actions, thereby enhancing coordination efficiency, task performance, and resource management in multi-agent collaboration scenarios. Evaluation was done on simulated experiments in the OpenAI Multi-Agent Particle Environment and Kaggle Drone Swarm Coordination datasets. The recursive update was done using a gradient-based policy refinement approach that facilitated selective information exchange between heterogeneous agents and adaptive behavior improvement. The suggested model showed an average TCR of 92.6%, CE of 88.4%, and RU of 91.3%, surpassing traditional algorithms such as a static heuristic model where TCR is 72.4%, CE 68.3%, and RU 79.5%. The convergence rate was optimized from 35 iterations in static heuristic models to 15 iterations, compared to 28 iterations in non-recursive reinforcement learning algorithms. The convergence pattern revealed fast policy convergence, especially the TCR, which demonstrates statistically significant differences between both methods. Recursive strategy improvement facilitates efficient multi-agent coordination by converging fast, minimizing conflicts, and optimizing resource allocation.