17951870. ADVERSARIAL ATTACKS FOR IMPROVING COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING SYSTEMS simplified abstract (International Business Machines Corporation)
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 17951870 titled 'ADVERSARIAL ATTACKS FOR IMPROVING COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING SYSTEMS
Simplified Explanation
The abstract describes a method for training a dynamics model of a cooperative multi-agent reinforcement learning (c-MARL) environment, generating a state perturbation, selecting vulnerable agents, and attacking the c-MARL system based on the perturbation and selected agents.
- Training a dynamics model of a c-MARL environment
- Processing a perturbation optimizer to generate a state perturbation
- Selecting vulnerable agents within the c-MARL system
- Attacking the c-MARL 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 identify vulnerabilities in cooperative multi-agent systems and test their robustness against attacks.
Benefits
- Improved security testing for multi-agent systems - Enhanced understanding of system vulnerabilities - Potential for developing more secure multi-agent systems
Potential Commercial Applications
Enhancing cybersecurity measures in various industries such as finance, healthcare, and defense.
Possible Prior Art
There may be prior art related to reinforcement learning techniques in multi-agent systems, 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 and efficiency of this new approach.
What are the potential limitations or challenges of implementing this method in real-world scenarios?
The abstract does not address any potential obstacles or limitations that may arise when applying this method in practical settings, such as scalability or computational resources.
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.
