Reinforcement Network Feedback for Non-Differentiable Multi-Stage Systems
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Solution Overview
Problem
Multi-stage systems, such as those used in multi-person pose detection tasks, are often non-differentiable, preventing joint optimization and resulting in suboptimal performance due to the inability to calculate gradients, especially when operations like cropping an image within a bounding box are performed.
Innovation Solution
A system reinforcement learning method that processes an input image through a first network, feeds the result into a second network, and uses a reinforcement network to adjust the output based on feedback, optimizing the results through iterative processes and adjustment actions to achieve a target outcome.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a multi-stage system is used for tasks like multi-person pose detection, then the system achieves good detection results and flexibility, but the system becomes non-differentiable and cannot perform joint optimization
Solution Approach 1:
The patent introduces a reinforcement learning mechanism where the second network's output feeds back to adjust the first network's output. The reinforcement network calculates a reward signal based on the second network's results and uses this feedback to iteratively optimize the first network's bounding box predictions, enabling joint optimization across non-differentiable stages.
Solution Approach 2:
The patent introduces a reinforcement network as an intermediary component between the first and second networks. This mediator translates the non-differentiable output of the second network into actionable feedback signals that can guide the optimization of the first network, bridging the gap between discrete and continuous optimization domains.
2Adaptability or versatility
If operations like cropping an image within a bounding box are performed in a multi-stage system, then the system achieves functional complexity, but gradient calculation becomes impossible
Solution Approach 1:
The reinforcement learning framework provides a feedback mechanism that bypasses the need for gradient calculation through traditional backpropagation. The second network's performance feedback is used to iteratively adjust the first network's parameters, enabling optimization in systems with non-differentiable operations like cropping.
Solution Approach 2:
The patent replaces the traditional gradient-based mechanical optimization system with a reinforcement learning-based optimization system. Instead of relying on continuous gradient flow through differentiable operations, the system uses discrete reward signals and iterative policy optimization to achieve the same goal of parameter optimization.
Data Source
AI summary
A system reinforcement learning method includes: processing an input image based on a first network of a system to obtain a first result; inputting the first result to a second network of the system to obtain a second result; and obtaining a reinforcement operation based on the second result by means of a reinforcement network, and adjusting the first result based on the reinforcement operation to obtain a target result. According to the embodiments of the present disclosure, information is fed back from downstream to upstream by means of the reinforcement network, and an output result of the system is optimized.