18281606. SYSTEM AND METHOD FOR COORDINATING MULTIPLE AGENTS IN AN AGENT STOCHASTIC ENVIRONMENT simplified abstract (Telefonaktiebolaget LM Ericsson (publ))
Contents
- 1 SYSTEM AND METHOD FOR COORDINATING MULTIPLE AGENTS IN AN AGENT STOCHASTIC ENVIRONMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR COORDINATING MULTIPLE AGENTS IN AN AGENT STOCHASTIC ENVIRONMENT - 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
SYSTEM AND METHOD FOR COORDINATING MULTIPLE AGENTS IN AN AGENT STOCHASTIC ENVIRONMENT
Organization Name
Telefonaktiebolaget LM Ericsson (publ)
Inventor(s)
Perepu Satheesh Kumar of Chennai (IN)
SYSTEM AND METHOD FOR COORDINATING MULTIPLE AGENTS IN AN AGENT STOCHASTIC ENVIRONMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18281606 titled 'SYSTEM AND METHOD FOR COORDINATING MULTIPLE AGENTS IN AN AGENT STOCHASTIC ENVIRONMENT
Simplified Explanation
The abstract describes a method for multi-agent reinforcement learning in a system with a master node and multiple agents. The method involves ranking the agents based on the variability of their underlying stochastic processes, updating their local policies sequentially, executing actions based on the updated policies, and adjusting the ranking based on the actions taken.
- Ranking agents based on variability of stochastic processes
- Sequentially updating local policies of agents
- Conditionally updating policies based on expected next state of other agents
- Simultaneously executing actions based on updated policies
- Updating agent ranking in response to actions taken
Potential Applications
This technology could be applied in various fields such as autonomous driving, robotics, and game playing where multiple agents need to collaborate and learn from each other.
Problems Solved
1. Efficient coordination and learning among multiple agents. 2. Adaptation to changing environments and tasks.
Benefits
1. Improved decision-making and coordination among agents. 2. Enhanced learning capabilities in complex environments.
Potential Commercial Applications
Optimizing supply chain management, enhancing traffic flow in smart cities, and improving resource allocation in industrial settings.
Possible Prior Art
One possible prior art could be research on multi-agent reinforcement learning algorithms in the field of artificial intelligence and machine learning.
Unanswered Questions
How does this method handle communication between agents?
The abstract does not specify how communication between agents is facilitated in the learning process.
What computational resources are required for implementing this method?
The abstract does not provide information on the computational resources needed to execute the multi-agent reinforcement learning algorithm effectively.
Original Abstract Submitted
A method of performing multi-agent reinforcement learning in a system including a master node and a plurality of agents that execute actions on an environment based on respective local policies of the agents is provided. The method includes generating a ranking of the plurality of agents based on levels of variability of stochastic processes underlying the behavior of respective ones of the plurality of agents, sequentially updating the local policies of the agents in order based on the ranking, wherein the local policy of a selected agent is updated conditioned on an expected next state of at least one previously selected agent, simultaneously executing actions by agents based on their updated local policies, and updating the ranking of the plurality of agents in response to executing the actions.
