MARL Policy Model for Inter-Agent Communication Control
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Solution Overview
Problem
Existing multi-agent reinforcement learning (MARL) systems face challenges in efficiently managing inter-agent communication due to resource constraints such as bandwidth and power capacity, leading to costly unconstrained communication in applications like the Internet of Things and robotics.
Innovation Solution
A MARL system that trains agents to control when and with whom to communicate by using aggregated reward signals combining task rewards and communication penalties, optimizing communication through a policy model that determines whether and to whom to send messages based on a reward score that balances communication value and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If unconstrained inter-agent communication is implemented, then agents can share information and agree on joint strategies, but resource consumption (bandwidth, power capacity) increases significantly
Solution Approach 1:
The patent implements dynamic communication control where agents adapt their communication behavior based on real-time task requirements and resource conditions. The policy model dynamically determines communication actions (send, receive, or stay silent) rather than using fixed communication patterns, allowing the system to optimize the balance between information sharing and resource consumption during task execution
Solution Approach 2:
The patent changes the communication parameter from unconstrained to constrained by introducing a policy model that regulates communication based on task relevance. The reward function modifies communication behavior by penalizing unnecessary communications and rewarding task-effective information exchange, effectively changing the communication parameter from binary (communicate/don't communicate) to a continuous optimization problem
2Loss of information
If agents communicate with all other agents at every step, then complete information exchange is achieved, but communication cost increases
Solution Approach 1:
The patent extracts only the necessary communication interactions from the set of all possible agent interactions. The policy model evaluates each potential communication action and selectively executes only those that are task-relevant, removing unnecessary communications from the system while preserving essential information exchange pathways
Solution Approach 2:
The patent applies partial action by having agents communicate only when the expected information gain exceeds a threshold, rather than communicating with all agents at all times. The policy model determines the optimal subset of communication actions needed to achieve task objectives without the excess cost of complete communication
3Use of energy by moving object
If communication is minimized to save resources, then resource efficiency improves, but task coordination capability deteriorates
Solution Approach 1:
The patent implements feedback through the reward function that evaluates communication actions based on their impact on task progress. The reward signal provides feedback to the policy model, reinforcing communication behaviors that improve task coordination while penalizing communications that do not contribute to task objectives, enabling the system to learn optimal communication strategies that balance resource efficiency and coordination capability
Solution Approach 2:
The patent enables agents to self-regulate their communication behavior through the trained policy model. Each agent independently determines its own communication actions based on its local state and learned policies, allowing the system to achieve efficient coordination without centralized control while maintaining resource efficiency
Data Source
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AI summary
An agent in a multi-agent system is provided with a policy model that controls communication of the agent with other agents in the multi-agent system. The policy model is trained by using MARL. The policy model receives more messages from one or more other agents in the multi-agent system. The policy model generates a reward score based at least on a hidden state of the agent and the one or more messages. The reward score represents an aggregation of a value of sending the message for a task and a cost of sending the message. The policy model determines whether to send the message based on the reward score. After determining to send the message, the policy model generates the message based on the hidden state of the agent and the one or more messages and sends the message to one or more other agents in the multi-agent system.