Systems and methods of depolying reinforcement learning
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Solution Overview
Problem
Existing reinforcement learning algorithms struggle to effectively generalize across different application areas, particularly in telecommunications promotions, due to challenges in transforming raw data into an AI-friendly format and tailoring the algorithmic learning process to the specific task at hand, including issues with feature space discretization and reward signal scarcity.
Innovation Solution
A method is developed to transform customer data into discrete states using dynamic customer latent allocations and incorporate application-specific proxies for the reward function, optimizing the reinforcement learning process to enhance personalized marketing strategies in telecommunications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If continuous features are discretized to enable tabular reinforcement learning methods, then the method becomes more applicable to tabular data formats, but the number of states explodes combinatorially making training unviable
Solution Approach 1:
The patent segments the continuous feature space into discrete bins rather than creating a complete grid of all possible combinations. This selective discretization maintains manageability while capturing essential patterns, avoiding the combinatorial explosion that would occur with full grid discretization.
Solution Approach 2:
The patent employs dynamic binning where the discretization parameters adapt based on data characteristics and problem requirements. The bin edges and granularity are adjusted dynamically during the learning process to optimize performance while maintaining feasible state spaces.
2Adaptability or versatility
If Deep Q-learning is used to handle continuous features, then the method can process continuous data, but it requires a lot of training data and does not guarantee optimality
Solution Approach 1:
The patent changes the fundamental parameters of the reinforcement learning approach by using tabular methods with carefully designed discretization rather than deep neural networks. This parameter change reduces the data requirements significantly while maintaining effectiveness for the specific problem domain of telecommunications promotions.
Solution Approach 2:
The patent creates a simplified representation (copy) of the continuous state space through discretization into representative bins. This copied, simplified state space can be processed with tabular methods that require less data, capturing the essential dynamics without the computational burden of full continuous space exploration.
3Productivity
If reinforcement learning algorithms are applied directly without transformation, then the algorithmic learning process can proceed, but the raw data format is not AI-friendly and requires transformation
Solution Approach 1:
The patent performs preliminary data transformation and feature engineering before applying the reinforcement learning algorithm. Raw customer data is pre-processed into meaningful features and discretized states that are directly suitable for the RL algorithm, eliminating the need for complex on-the-fly transformations during learning.
Solution Approach 2:
The patent introduces an intermediary layer of processed features and latent representations that bridge the raw data and the reinforcement learning algorithm. This intermediary transformation layer simplifies the data format to be AI-friendly while maintaining the essential information needed for effective learning.
4Adaptability or versatility
If generic reinforcement learning algorithms are used without task-specific tailoring, then the algorithm can be applied broadly, but it cannot effectively generalize across different application areas
Solution Approach 1:
The patent applies local quality by customizing the discretization schemes, feature selections, and reward formulations to match the specific characteristics of telecommunications promotion data. This localized adaptation ensures reliable generalization within the domain while maintaining the core RL framework's versatility.
Solution Approach 2:
The patent changes key parameters such as the state discretization bins, action spaces, and reward functions to be specific to the telecommunications domain. These parameter adjustments enable the algorithm to capture domain-specific patterns and generalize effectively across different promotion scenarios within the industry.
Data Source
AI summary
Methods and systems of using reinforcement learning to optimizing promotions. A promotion can be offered to a user using a reinforcement learning model with a sensitivity parameter, the reinforcement module estimating a time period during which the user will respond to the first information. The user's reaction to the promotion can be observed. The reinforcement learning model can be adapted based on the user's reaction.