Multi-Agent Reinforcement Learning Policy Update with Priority Constraints

The patent segments the action space by introducing priority levels for different agents. Instead of treating all agents' actions as a single high-dimensional space, the action space is divided into prioritized subsets where high-priority agents' actions are determined first, followed by low-priority agents. This segmentation reduces the effective dimensionality that each agent needs to learn independently.

The patent adds a priority dimension to the multi-agent system. By introducing priority levels as an additional dimension, the system transforms the exponential growth problem in action space into a manageable hierarchical structure. The priority dimension allows the system to organize agents in a sequence, converting the combinatorial explosion of joint action spaces into a sequential decision-making process.

The patent applies preliminary action by determining priority levels for all agents before the learning process begins. This pre-established priority structure guides the learning process, ensuring that agents learn their roles and constraints in advance. The priority determination is performed beforehand, creating a framework that guarantees performance constraints will be satisfied during and after learning.

The patent implements feedback mechanisms where the learned policies are evaluated against system-wide constraints. The priority-based structure provides feedback on which agents' actions are more critical, allowing the system to adjust and refine policies while maintaining constraint satisfaction. This feedback loop ensures that performance guarantees are maintained throughout the learning process.