Multi-Agent RL Framework for Cooperative Policy Optimization
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Solution Overview
Problem
In multi-agent systems, independent optimization by agents can lead to non-optimal results due to goal mismatches and lack of information sharing, resulting in suboptimal outcomes for the system as a whole.
Innovation Solution
A reinforcement learning (RL) simulation framework is developed where each agent collects and shares information to calibrate rewards, allowing agents to cooperate and optimize their operations, with mechanisms for adjusting global information sharing to maximize overall benefits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If agents operate independently to maximize their own objectives, then each agent can achieve optimal local goals, but the system as a whole produces non-optimal results due to goal mismatches and lack of information sharing
Solution Approach 1:
The patent implements feedback mechanisms where agents receive information about the global state and other agents' actions. This feedback loop allows agents to adjust their local decisions based on system-wide consequences, resolving the contradiction between independent operation and system optimization by enabling agents to learn from collective outcomes while maintaining operational independence
Solution Approach 2:
The patent introduces a central coordinator or communication infrastructure as an intermediary that facilitates information exchange between independently operating agents. This mediator enables goal alignment and coordination without requiring agents to directly control each other, thus preserving operational independence while achieving system-wide optimization
2Productivity
If agents share all global information, then the system can achieve optimal coordination, but individual agents may become greedy and take advantage of global information to the detriment of other entities
Solution Approach 1:
The patent applies local quality by providing different levels of information access to different agents based on their roles and needs. Instead of uniform global information sharing, agents receive customized information sets that are sufficient for their local optimization tasks but limited enough to prevent exploitative greedy behavior, thus achieving coordination efficiency while maintaining fairness
Solution Approach 2:
The patent dynamically adjusts information sharing parameters such as the amount, type, and frequency of global information provided to agents. By changing these parameters based on system state and agent behavior, the system can prevent greedy exploitation while maintaining adequate coordination, resolving the contradiction between coordination efficiency and prevention of harmful behaviors
3Productivity
If agents cooperate frequently to share and aggregate information, then the multi-agent system can be optimized, but the complexity of coordination and communication increases
Solution Approach 1:
The patent implements periodic information sharing and coordination among agents rather than continuous interaction. Agents exchange information and coordinate actions at regular intervals or triggered by specific events, which reduces communication overhead and coordination complexity while still achieving system optimization through periodic alignment of local and global goals
Data Source
AI summary
Disclosed herein are methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modeling agents in multi-agent systems as reinforcement learning (RL) agents and training control policies that cause the agents to cooperate towards a common goal. A method can include generating, for each of a group of simulated local agents in an agent network in which the simulated local agents share resources, information, or both, experience tuples having a state for the simulated local agent, an action taken by the simulated local agent, and a local result for the action taken, updating each local policy of each simulated local agent according to the respective local result, providing, to each of the simulated local agents, information representing a global state of the agent network, and updating each local policy of each simulated local agent according to the global state of the agent network.