Reinforcement Learning Random Number Generator
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Solution Overview
Problem
Existing methods for generating random numbers are limited, as they often produce the same random number when using the same seed value, and lack the ability to generate different random numbers using reinforcement learning-based generative models.
Innovation Solution
A method and apparatus for generating dynamic random numbers using reinforcement learning, which involves an agent unit generating a first random number based on a deep learning-based model, calculating similarities with previously stored random numbers, and performing reinforcement learning to adjust the model and generate different random numbers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a predetermined value is calculated according to a predetermined input using a seed value, then the generation process is simple and deterministic, but the same random number is generated when the same seed value is used
Solution Approach 1:
The patent applies dynamics by making the random number generation process adaptive rather than static. The reinforcement learning agent continuously learns from feedback about generated random numbers and adjusts its behavior over time. Even with the same seed input, the system evolves its generation capabilities through repeated interactions, allowing different random numbers to be produced while maintaining the simplicity of the input process.
Solution Approach 2:
The patent changes parameters by introducing a reinforcement learning agent that modifies the generation process based on learned patterns. The system transitions from a fixed deterministic calculation to a dynamic process where the agent's policies and value functions evolve. This allows the same seed value to produce different random numbers as the system learns and adapts its internal parameters through training.
2Productivity
If traditional random number generation methods are used, then the process is computationally efficient, but the generation rules can be inferred by third parties
Solution Approach 1:
The patent introduces a reinforcement learning agent as an intermediary between the seed input and the random number output. This agent acts as a black box that processes inputs through learned policies and value functions, making the internal generation rules difficult to infer. The agent's complex decision-making process obscures the relationship between inputs and outputs while maintaining computational efficiency through learned patterns rather than exhaustive calculations.
Solution Approach 2:
The patent adds a temporal dimension to the random number generation process by incorporating reinforcement learning training and adaptation over time. The system doesn't just generate random numbers based on static rules but evolves its generation capabilities across multiple interactions. This temporal evolution creates a dynamic system where patterns are continuously changing, making rule inference by third parties significantly more difficult while maintaining computational efficiency through learned policies.
3Adaptability or versatility
If reinforcement learning is applied to generate random numbers, then different random numbers can be generated with the same input, but the generation process becomes more complex
Solution Approach 1:
The patent applies self-service by enabling the system to automatically improve its random number generation capabilities through reinforcement learning. The agent learns optimal generation strategies by interacting with the environment and receiving feedback, eliminating the need for manual configuration or complex external control mechanisms. This self-learning approach manages complexity by allowing the system to adapt autonomously rather than requiring intricate predetermined rules.
Solution Approach 2:
The patent implements feedback mechanisms where the reinforcement learning agent receives information about generated random numbers and uses this feedback to adjust its future generation behavior. The environment provides compensation signals that guide the agent's learning process, allowing it to refine its policies and value functions over time. This feedback loop manages complexity by providing clear directional guidance rather than requiring the system to navigate all possible generation paths independently.
Data Source
AI summary
Provided are an apparatus and method for generating random numbers based on reinforcement learning. The method is implemented with a computer, and includes: generating a first random number based on a deep learning-based first model through an agent unit; calculating respective similarities between at least one previously stored second random number and the first random number; performing reinforcement learning based on generative behavior of the first random number of the agent unit based on the respective similarities; and storing the first random number in a memory. The performance of the reinforcement learning is configured to provide compensation information based on the respective similarities to the agent unit through an environment unit, and then to control the agent unit to learn the first model based on the first random number.