Neural Network Action Selection via Gaussian Value Distribution
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Solution Overview
Problem
Existing neural network devices struggle to select optimal actions based on current states effectively, particularly in complex environments where multiple actions need to be evaluated.
Innovation Solution
A neural network device and method that utilize deep learning to calculate value distributions for each action, incorporating Gaussian graphs to represent value returns, and adaptively determine a weight kernel to optimize action selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing neural network devices are used for action selection, then the device structure is simple, but the action selection accuracy is insufficient in complex environments
Solution Approach 1:
The neural network device is segmented into multiple specialized modules: a deep learning module for calculating value distributions, a post-processing module for action selection, and a kernel determination module for adaptive weight adjustment. This segmentation allows each module to perform its specific function with high precision while maintaining overall system manageability.
Solution Approach 2:
The patent introduces Gaussian graphs as a new dimensional representation for value distributions, transforming traditional scalar value outputs into multi-dimensional probability distributions. This dimensional enhancement allows the network to capture uncertainty and variability in action values, significantly improving selection accuracy in complex environments.
2Measurement precision
If deep learning is applied to calculate value distributions for multiple actions, then the action selection accuracy improves, but the calculation complexity increases
Solution Approach 1:
The kernel determination module automatically adapts weight kernels based on the characteristics of value distributions without requiring manual intervention or complex external optimization processes. The system performs self-adjustment by minimizing distance differences between predicted and actual value distributions, reducing the need for complex external control mechanisms.
Solution Approach 2:
The patent dynamically changes the parameters of Gaussian graphs (mean, standard deviation) based on the calculated value distributions. By adjusting these parameters adaptively, the system achieves high calculation accuracy while maintaining computational efficiency through parameter optimization rather than structural complexity.
3Reliability
If Gaussian graphs are used to represent value distributions, then the decision-making accuracy in dynamic environments improves, but the data processing complexity increases
Solution Approach 1:
The post-processing module receives feedback from the deep learning module about value distribution characteristics and uses this feedback to adjust action selection strategies. The kernel determination module also uses feedback from distance difference calculations to iteratively refine weight kernels, improving decision reliability through continuous feedback loops rather than complex one-time processing.
Solution Approach 2:
The patent replaces traditional mechanical decision-making mechanisms with probabilistic Gaussian graph representations. Instead of deterministic rules, the system uses probability distributions to represent uncertainty and enable more reliable decisions in dynamic environments, substituting rigid mechanical logic with flexible statistical modeling.
Data Source
AI summary
A neural network device and an action selecting method using the same, which select an action corresponding to a current state on the basis of a value return. A method of selecting, executed by at least one processor, an action on the basis of deep learning includes receiving a current state as an input, calculating a value distribution corresponding to each of a plurality of actions to be performed on the current state, and selecting an optimal action from among the plurality of actions based on the value distribution, wherein the value distribution includes at least one Gaussian graph following a Gaussian distribution.