Page Layout Policy Using RL and Imitation Learning Rules
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Solution Overview
Problem
Existing automatic page layout technologies struggle to generate layouts that conform to both layout rules and user aesthetics, particularly when dealing with multiple page elements, often resulting in overlapping or improperly placed elements.
Innovation Solution
A page layout method utilizing reinforcement learning to enforce layout rules and imitation learning to enhance aesthetics, combining a first-priority hard rule with a second-priority soft rule, and incorporating user interaction to optimize the layout process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random layout mode is used to deploy page elements, then the layout process is simple and fast, but the generated layouts do not conform to layout rules and elements may overlap or be improperly placed
Solution Approach 1:
The layout process is segmented into two distinct phases: reinforcement learning phase for generating candidate layouts that satisfy hard rules, and imitation learning phase for optimizing aesthetic quality. This segmentation allows each phase to focus on specific requirements without compromising the other.
Solution Approach 2:
The reinforcement learning algorithm performs preliminary action by generating candidate layouts that satisfy hard layout rules before the imitation learning phase optimizes aesthetic quality. This preliminary constraint satisfaction ensures rule conformity is built into the foundation of the layout process.
2Quantity of substance
If the quantity of page elements and page size increase, then the layout complexity increases, but the likelihood of generating non-conforming layouts with overlaps or boundary violations increases
Solution Approach 1:
The reinforcement learning algorithm incorporates feedback mechanisms where the environment evaluates each layout action against hard rules and provides reward signals. This continuous feedback ensures that even as element quantity increases, the algorithm learns to maintain rule conformity through iterative optimization.
Solution Approach 2:
The system changes parameters by using different reward functions and state representations that scale with the number of elements. The reinforcement learning algorithm adapts its policy based on changing parameters such as element count and page dimensions, maintaining reliability through dynamic parameter adjustment.
3Manufacturing precision
If only reinforcement learning is used for page layout, then layout rules are enforced, but user aesthetic preferences are not adequately considered
Solution Approach 1:
The patent merges reinforcement learning and imitation learning into a unified two-stage framework. Reinforcement learning enforces hard rules while imitation learning adapts to user aesthetic preferences, combining the strengths of both approaches to achieve both rule conformity and aesthetic quality.
Solution Approach 2:
The imitation learning component acts as an intermediary between the reinforcement learning algorithm and user aesthetic preferences. It translates subjective aesthetic criteria into learnable patterns from demonstration data, mediating between rule-based generation and preference-based optimization.
4Extent of automation
If manual design work is replaced by automatic layout, then labor consumption and training time are reduced, but generating layouts that conform to rules becomes difficult
Solution Approach 1:
The patent replaces manual mechanical design work with an automated intelligent system combining reinforcement learning and imitation learning. This substitution maintains high rule conformity by using algorithmic constraint satisfaction rather than human judgment, achieving both automation and precision.
Solution Approach 2:
The automated layout system uses a composite approach by combining two different learning paradigms (reinforcement learning and imitation learning) into a single system. This composite structure allows the system to simultaneously achieve rule enforcement and aesthetic quality, overcoming the limitations of using either approach alone.
Data Source
AI summary
A page layout method includes obtaining page information, element information of at least one page element to be laid out, and a layout rule; obtaining at least one candidate page layout policy based on the page information, the element information, and the layout rule using a reinforcement learning algorithm; and determining a target page layout policy from the at least one candidate page layout policy using an imitation learning algorithm.