DAG Recommendation Dimensions for RL Training Complexity
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Solution Overview
Problem
Reinforcement learning agents face difficulties in effectively exploring and training in large recommendation spaces with multiple decisions, leading to inefficiencies in selecting optimal actions.
Innovation Solution
The use of a directed acyclic graph (DAG) configuration, where multiple reinforcement learning agents compute values for different dimensions, allowing for the computation of actions by leveraging the dependencies and order specified in the DAG, thereby reducing the complexity of the decision-making process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reinforcement learning agents explore large recommendation spaces with multiple decisions, then the quality of recommendations improves, but the training complexity and time required increases significantly
Solution Approach 1:
The patent segments the recommendation space into multiple independent dimensions (e.g., product category, price range, brand preference). Instead of having a single agent explore the entire large state-action space, multiple specialized agents are created, each responsible for learning optimal actions for a specific dimension. This segmentation reduces the effective state space each agent must explore, thereby reducing training complexity while maintaining overall recommendation quality.
Solution Approach 2:
The patent introduces a new dimensional structure by organizing agents along the dimension of recommendation attributes. Rather than expanding the state space horizontally (more states per dimension), the system expands vertically by adding multiple agent instances, each operating in a reduced state space corresponding to their specific dimension. This dimensional transformation converts a single complex learning problem into multiple simpler parallel learning problems.
2Measurement precision
If reinforcement learning agents explore large recommendation spaces with multiple decisions, then the quality of recommendations improves, but the training time required increases significantly
Solution Approach 1:
By segmenting the recommendation task into dimension-specific subtasks handled by specialized agents, each agent trains on a smaller, more focused state space. This reduces the number of training iterations required per agent compared to a single agent learning the entire complex space, thereby reducing total training time while achieving comparable or superior recommendation quality through the integration of multiple specialized perspectives.
Solution Approach 2:
The patent enables parallel training of multiple agents simultaneously, each performing useful learning actions independently without interfering with others. This parallelization allows continuous progress across all dimensions concurrently, rather than sequentially, significantly reducing the total wall-clock training time required to achieve comprehensive recommendation coverage.
3Productivity
If multiple reinforcement learning agents are used to compute values for different dimensions, then the exploration efficiency improves, but the system complexity increases
Solution Approach 1:
The system segments the complex recommendation task into independent dimensional components, with each agent handling a specific segment. This segmentation improves exploration efficiency by allowing focused, specialized learning in each dimension rather than diluted attention across all dimensions. The resulting system complexity is managed through modular architecture, where each agent is a simple, identical template instantiated for different dimensions, reducing the complexity burden compared to a monolithic agent.
Solution Approach 2:
The patent employs universal agent templates that can be instantiated for different dimensions with the same underlying architecture and learning algorithm. This universality allows the system to scale to multiple dimensions without proportionally increasing system complexity, as each new dimension simply adds another instance of the proven agent template rather than requiring a completely new agent design. The multi-functionality of the template handles various dimension types through parameter configuration rather than structural changes.
Data Source
AI summary
A computer accesses a dataset for computing an action including multiple decision items and a configuration for a directed acyclic graph (DAG) comprising nodes representing dimensions. Each dimension is associated with a decision item of the action. The computer computes a value for a first decision item associated with a top level dimension of the DAG based on the dataset and using a reinforcement learning agent for the top level dimension. The computer computes a value for a second decision item associated with a non-top level dimension from the DAG based on the dataset and a specified subset of outputs of reinforcement learning engines for dimensions upstream from the non-top level dimension in the DAG and using a reinforcement learning agent for the non-top level dimension. The computer provides the computed action including the value for the first decision item and the value for the second decision item.