Reinforcement Learning Policy Adjustment for Content Slot Bidding

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional systems struggle to optimize content presentation in large-scale online environments, particularly in managing complex user behaviors and preferences, while balancing power consumption, network bandwidth, and computing performance, especially when content is dynamically provided by multiple parties.

Innovation Solution

An online system employs a reinforcement learning algorithm to adjust policy functions, monitoring and refining content presentation campaigns to align realized outcomes with desired objectives set by content providers, using a bidding process that automatically optimizes content slots.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional systems are used to manage content presentation in large-scale online systems, then system simplicity is maintained, but the ability to optimize for user preferences, power consumption, network bandwidth, and computing performance deteriorates

Engineering Contradiction:

Improveoptimization capability for user preferences and system resourcesVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system employs reinforcement learning algorithms that enable automated policy adjustment without manual intervention. The learning algorithm autonomously optimizes content presentation policies by learning from observed outcomes and automatically adjusting parameters to achieve desired objectives, eliminating the need for complex manual configuration and management interfaces.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The reinforcement learning algorithm dynamically adjusts policy parameters based on learned patterns and observed system states. By continuously modifying parameters such as content selection criteria, presentation timing, and resource allocation based on learned optimal values, the system achieves adaptive optimization without requiring complex manual parameter management.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If manual adjustment of content presentation factors is performed, then system complexity is reduced, but the speed and effectiveness of optimization deteriorates

Engineering Contradiction:

Improveoptimization speed and effectivenessVSAvoidautomation level

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The system implements a feedback loop where the reinforcement learning algorithm continuously monitors the outcomes of content presentation decisions, compares realized outcomes against desired objectives, and uses this feedback to automatically adjust policy parameters. This closed-loop control enables rapid iterative optimization without manual intervention, achieving both high automation and high productivity.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The reinforcement learning algorithm performs preliminary learning and policy optimization in advance through continuous training on historical data and simulated environments. This pre-learning capability allows the system to quickly adapt to new content presentation scenarios without requiring manual reconfiguration, thereby increasing optimization speed while maintaining high automation levels.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If complex data relationships are tracked to understand user preferences, then recommendation quality improves, but the difficulty of monitoring and quantifying preferences increases

Engineering Contradiction:

Improveuser preference measurement accuracyVSAvoidpreference monitoring difficulty

Core Design Contradiction:

Measurement precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The system replaces manual preference monitoring and analysis mechanisms with reinforcement learning-based automated detection. The algorithm automatically learns user preferences by analyzing interaction patterns and behavioral data, transforming the complex task of preference measurement into an automated learning process that achieves high precision without increasing operational difficulty.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12511677B2Automated policy function adjustment using reinforcement learning algorithm

Publication Date: 2025.12.30 MAPLEBEAR INC

AI summary

An online system may receive, from a content provider, a content presentation campaign that includes one or more objectives. The online system may define a set of one or more policy functions that automatically controls the content presentation campaign. A policy function may control one or more criteria in bidding content slots. The online system may monitor a realized outcome of the content presentation campaign. The online system may apply a reinforcement learning algorithm in adjusting the set of policy functions. The reinforcement learning algorithm adjusts one or more parameters in the set of policy functions to reduce a difference between the realized outcome and the desired outcome set by the content provider. The online system generates an adjusted set of policy functions and uses the adjusted set of policy functions in bidding content slots to present one or more content items provided by the content provider.