International business machines corporation (20240119298). ADVERSARIAL ATTACKS FOR IMPROVING COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING SYSTEMS simplified abstract
Contents
- 1 ADVERSARIAL ATTACKS FOR IMPROVING COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING SYSTEMS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ADVERSARIAL ATTACKS FOR IMPROVING COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING SYSTEMS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
ADVERSARIAL ATTACKS FOR IMPROVING COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING SYSTEMS
Organization Name
international business machines corporation
Inventor(s)
Nhan Huu Pham of Tarrytown NY (US)
Lam Minh Nguyen of Ossining NY (US)
Jie Chen of Briarcliff Manor NY (US)
Thanh Lam Hoang of Maynooth (IE)
Subhro Das of Cambridge MA (US)
ADVERSARIAL ATTACKS FOR IMPROVING COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING SYSTEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119298 titled 'ADVERSARIAL ATTACKS FOR IMPROVING COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING SYSTEMS
Simplified Explanation
The patent application describes a method for training a dynamics model of a cooperative multi-agent reinforcement learning environment, generating a state perturbation, selecting vulnerable agents, and attacking the system based on the perturbation.
- Training a dynamics model of a cooperative multi-agent reinforcement learning environment
- Processing a perturbation optimizer to generate a state perturbation
- Selecting one or more agents with enhanced vulnerability
- Attacking the system based on the state perturbation and selected agents
Potential Applications
This technology could be applied in cybersecurity for testing the resilience of multi-agent systems against attacks.
Problems Solved
This technology helps in identifying vulnerabilities in cooperative multi-agent systems and testing their robustness against attacks.
Benefits
The method provides a systematic way to assess the security of multi-agent systems and improve their defenses against potential threats.
Potential Commercial Applications
"Enhancing Security in Multi-Agent Systems through State Perturbation Attacks"
Possible Prior Art
There may be prior art related to perturbation attacks in the context of reinforcement learning environments, but specific examples are not provided in the abstract.
Unanswered Questions
How does this method compare to traditional vulnerability testing techniques in multi-agent systems?
The article does not provide a comparison with traditional vulnerability testing methods to evaluate the effectiveness of this new approach.
Are there any limitations or constraints in implementing this method in real-world scenarios?
The article does not address any potential limitations or challenges that may arise when implementing this method in practical applications.
Original Abstract Submitted
in aspects of the disclosure, a method comprises training, by a computing system, a dynamics model of a cooperative multi-agent reinforcement learning (c-marl) environment. the method further comprises processing, by the computing system, a perturbation optimizer to generate a state perturbation of the c-marl environment, based on the dynamics model. the method further comprises selecting one or more agents of the c-marl system as having enhanced vulnerability. the method further comprises attacking, by the computing system, the c-marl system based on the state perturbation and the selected one or more agents.
