Deep Reinforcement Learning Agent for Dynamic Job Provisioning
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Solution Overview
Problem
Existing methods for job provisioning face challenges in efficiently balancing multiple key performance indicators (KPIs) due to exponential complexity, leading to suboptimal results, especially in complex job scenarios where brute force calculations are impractical and simple heuristics yield poor quality outcomes.
Innovation Solution
A dynamic provisioning agent using machine-learning, specifically reinforcement learning, is trained to optimize KPIs by modeling the provisioning process as a Markov decision process, employing Q-learning and deep reinforcement learning with neural networks to select optimal actions based on scoring functions, thereby improving provisioning efficiency and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If brute force calculation is used to evaluate each possible combination, then provisioning quality is maximized, but computational complexity becomes exponential and impractical
Solution Approach 1:
The patent replaces traditional mechanical optimization approaches (brute force calculation, linear optimization, simple heuristics) with a machine learning-based system. A neural network model is trained to predict optimal provisioning decisions directly from input features, substituting the computational mechanics of evaluating all possible combinations with a learned prediction function that provides high-quality results in constant time regardless of problem size.
2Device complexity
If simple heuristics are used to solve provisioning, then computational complexity is reduced, but provisioning quality becomes poor for complex jobs
Solution Approach 1:
The patent transforms the provisioning problem by changing the parameters from discrete combinatorial choices to continuous probability distributions predicted by a neural network. The system outputs provisioning decisions as probability distributions over possible actions, which are then sampled to generate actual provisioning plans. This parameter transformation allows the system to capture complex relationships while maintaining computational efficiency.
3Adaptability or versatility
If traditional optimization methods are used, then provisioning decisions can be made, but adaptability to changing demands and complex scenarios is limited
Solution Approach 1:
The patent implements a dynamic provisioning system where the neural network model can adapt to changing conditions through its probabilistic output framework. The system dynamically adjusts provisioning decisions by sampling from probability distributions that reflect current system state and learned patterns, enabling flexible adaptation to new job types, resource conditions, and demand patterns without retraining the entire model.
Data Source
AI summary
A system and a process for provisioning a job through a trained machine-learning dynamic provisioning agent is provided herein. An input vector representing the job having one or more job components may be received. One or more additional data vectors representing additional job data may be obtained. For the one or more job components respectively, one or more action values corresponding to one or more provisioning options may be calculated based on the one or more additional data vectors. For the one or more job components respectively, one or more provisioning options for the respective one or more job components may be selected based on the corresponding one or more action values. The one or more selected provisioning options corresponding to the respective one or more job components may be aggregated. The aggregated selected provisioning options may be provided.