International business machines corporation (20240161002). QUANTUM REINFORCEMENT LEARNING AGENT simplified abstract

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 20240161002 titled 'QUANTUM REINFORCEMENT LEARNING AGENT

Simplified Explanation

The patent application describes systems, computer-implemented methods, and computer program products that facilitate applying a reinforcement learning policy to available actions.

• A system comprises a memory storing computer executable components and a processor executing these components.
• The components include a state encoder mapping an environment state onto qubits of a quantum device based on encoding parameters.
• A variational component combines a reinforcement learning policy with qubit sampling, resulting in 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, gaming, and financial trading where decision-making based on reinforcement learning policies is crucial.

Problems Solved

This technology addresses the challenge of efficiently applying reinforcement learning policies to available actions in complex environments, improving decision-making processes and optimizing outcomes.

Benefits

The system offers a more effective and optimized way of implementing reinforcement learning policies, leading to better decision-making, enhanced performance, and potentially higher success rates in various applications.

Potential Commercial Applications

• "Optimizing Decision-Making with Reinforcement Learning Policies in Autonomous Vehicles"
• "Enhancing Performance in Financial Trading through Quantum-Based Reinforcement Learning"

Possible Prior Art

One potential prior art could be the use of classical computing systems for implementing reinforcement learning policies, which may not offer the same level of efficiency and optimization as quantum-based systems.

What are the specific encoding parameters used in the state encoder component of the system?

The specific encoding parameters used in the state encoder component are not detailed in the abstract. Further information from the full patent application may provide insights into the exact parameters utilized.

How does the variational component determine the probability distribution of available actions?

The abstract does not elaborate on the exact method through which the variational component determines the probability distribution of available actions. A more in-depth exploration of the patent application could shed light on the specific mechanisms involved in this process.

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.