18163907. QUANTUM REINFORCEMENT LEARNING AGENT simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
- 1 QUANTUM REINFORCEMENT LEARNING AGENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 QUANTUM REINFORCEMENT LEARNING AGENT - 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
QUANTUM REINFORCEMENT LEARNING AGENT
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Peng Liu of Yorktown Heights NY (US)
Shaohan Hu of Yorktown Heights NY (US)
Stephen Wood of Thornwood NY (US)
Marco Pistoia of Amawalk NY (US)
Arthur Giuseppe Rattew of St. Louis MO (US)
QUANTUM REINFORCEMENT LEARNING AGENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18163907 titled 'QUANTUM REINFORCEMENT LEARNING AGENT
Simplified Explanation
The abstract describes a system that applies a reinforcement learning policy to available actions using quantum devices.
- The system includes a memory and a processor for executing computer executable components.
- A state encoder maps the state of an environment onto qubits of a quantum device based on encoding parameters.
- A variational component combines a reinforcement learning policy with qubit sampling to generate a probability distribution of available actions at the state of the environment.
Potential Applications
This technology could be applied in various fields such as robotics, autonomous vehicles, and game playing where decision-making based on available actions is crucial.
Problems Solved
This technology helps in efficiently applying reinforcement learning policies to available actions in complex environments, improving decision-making processes and optimizing outcomes.
Benefits
The system offers a more advanced and efficient way of decision-making in dynamic environments, leading to better performance and results.
Potential Commercial Applications
With its ability to enhance decision-making processes, this technology could find applications in industries such as finance, healthcare, and manufacturing for optimizing operations and resource allocation.
Possible Prior Art
Prior art in the field of reinforcement learning and quantum computing may exist, but specific examples are not provided in this abstract.
Unanswered Questions
How does the system handle large-scale environments with a high number of available actions?
The abstract does not mention how the system scales to handle environments with a large number of available actions and complex state spaces.
What are the potential limitations or challenges of implementing this technology in real-world scenarios?
The abstract does not address any potential limitations or challenges that may arise when implementing this technology in practical applications.
Original Abstract Submitted
Systems, computer-implemented methods, and computer program products that can facilitate applying a reinforcement learning policy to available actions are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a state encoder that maps, based on one or more encoding parameters, a state of an environment on to one or more qubits of a quantum device. The system can further comprise a variational component that combines a reinforcement learning policy with a sampling of the one or more qubits, resulting, based on one or more variational parameters, in a probability distribution of a plurality of available actions at the state of the environment.
