We deal with the problem of incorporating the most recent information in the system which may be crucial to update the system state, while maintaining resilience to faulty measurements. For this purpose, we propose and analyze a consensus protocol biased towards the latest information in the system. The convergence to consensus of the system is analyzed and two main approaches are introduced with stability ranges provided from sufficient conditions. A detailed analysis of weight-changing that achieves bias towards novelty in the system is provided. Numerical simulations demonstrate that final value and convergence rate of the system can be controlled. Finally, we provide system demonstration in a simple use-case example.

This paper presents a method for extending decision-making policies in active perception tasks for multi-agent systems within partially observable environments. Multiple agents obtain their policies by training in an environment of a certain size. Those policies are then used in the environments larger in size, that are divided into sub-environments of size similar (or smaller) to one that the agents were trained in. Learned policies are adapted accordingly by proposed Action-Space Reduced Policy (ASRP). By leveraging Multi-Agent Reinforcement Learning (MARL) within a POMDP framework, agents can use their learned policies across environments of differing complexity without requiring retraining. The consensus mechanism allows agents to maintain a common belief state, supporting collaborative decision-making based on observations from all agents involved. Validation of the method is conducted on the scenario of multi-agent exploration missions, demonstrating the use of extended policies and enhanced perception accuracy. Simulation results indicate expected success rates and decision-making times, regardless of the environment’s dimensionality. Potential applications for scalable, multi-agent perception systems are discussed, along with directions for future research.