Risk-Aware Operation Rule Learning Using Cumulative Reward Frequency
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Solution Overview
Problem
Reinforcement learning systems lack a method to determine operations considering risk, which is essential for safe decision-making in dynamic environments.
Innovation Solution
An operation rule determination device and method that includes an environment execution unit and a risk-considered history generation unit, which calculates a cumulative degree of operations and adjusts the degree of desirability based on risk information, allowing for risk-aware decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is used to determine operations, then decision-making speed and adaptability are improved, but risk consideration capability deteriorates
Solution Approach 1:
The patent segments the reward signal into two distinct components: cumulative reward (long-term outcome) and frequency of cumulative reward (short-term risk indicator). This segmentation allows the reinforcement learning system to process risk information separately from standard reward information, enabling risk-aware decision-making without sacrificing learning speed. The frequency component specifically tracks how often certain cumulative reward thresholds are exceeded, providing a risk metric that can be independently optimized.
Solution Approach 2:
The patent introduces frequency of cumulative reward as an intermediary variable that bridges the gap between standard reinforcement learning and risk consideration. This intermediary transforms the raw cumulative reward data into a risk indicator that can be integrated into the learning process. By using this intermediary, the system can maintain its standard reinforcement learning framework while adding risk awareness through the frequency metric, which modulates the learning updates based on risk levels.
2Reliability
If risk assessment is incorporated into the decision-making process, then safety is improved, but system complexity increases
Solution Approach 1:
The patent makes the frequency of cumulative reward calculation serve multiple functions: it acts as a risk indicator, a learning signal modifier, and a decision-making constraint. By designing this single mechanism to perform multiple roles, the patent avoids adding separate complex risk assessment modules. The frequency calculation integrates seamlessly with the existing reinforcement learning framework, demonstrating multi-functionality that reduces overall system complexity while maintaining safety improvements.
Solution Approach 2:
The patent changes the parameter representation by introducing frequency as an additional dimension to the cumulative reward parameter. Instead of creating a completely new risk assessment architecture, the system modifies the existing reward parameter space by adding frequency information. This parameter change allows the system to incorporate risk assessment using the same computational infrastructure, minimizing the increase in system complexity while enhancing safety capabilities.
Data Source
AI summary
An operation rule determination device includes an environment execution unit that obtains a state of a control target after each operation and the degree associated with the state for a series of operations on the control target, by using degree information in which the state and the degree of desirability of the state are associated with each other, and a risk-considered history generation unit that calculates a cumulative degree obtained by accumulating the obtained degree for the series of operations, and, when the cumulative degree satisfies a condition, reduces the degree associated with the state after the series of operations in the degree information.