Outer Loop Reinforcement Learning for Cost-Constrained Training
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Solution Overview
Problem
Existing reinforcement learning systems do not optimally consider operational costs and computational resource burdens, leading to inefficient training processes and high monetary costs.
Innovation Solution
A reinforcement learning framework that uses an outer loop process to manage and optimize financial and computational costs by adjusting settings such as license fees, simulation fidelity, and hardware usage, while employing a gradient estimator to optimize the training process of an inner loop neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reinforcement learning training sessions are conducted with high computational resource allocation and extended training duration, then learning accuracy and model performance are improved, but monetary costs and computational resource burdens increase significantly
Solution Approach 1:
The patent implements dynamic adjustment of training parameters including computational resource allocation, simulation fidelity levels, and training duration based on real-time monitoring of learning progress and budget constraints. The system transitions from static resource allocation to dynamic adaptation, modifying resource usage patterns during training sessions to optimize the balance between accuracy improvement and resource consumption.
Solution Approach 2:
The system changes multiple parameters simultaneously including simulation fidelity, time step granularity, budget allocation rates, and resource allocation levels. By adjusting these parameters dynamically based on learning stage and performance metrics, the system achieves high accuracy in critical phases while reducing resource burden during plateau periods or when diminishing returns are detected.
2Measurement precision
If higher simulation fidelity and more extensive training sessions are used, then solution accuracy is improved, but monetary costs increase
Solution Approach 1:
The patent applies partial action by using high simulation fidelity only when necessary for achieving target accuracy, rather than maintaining maximum fidelity throughout all training sessions. The system identifies critical training phases where high fidelity is needed and uses lower fidelity in other phases, achieving cost-effective accuracy improvement without excessive resource expenditure in all conditions.
Solution Approach 2:
Simulation fidelity is dynamically adjusted based on training progress, budget remaining, and performance requirements. The system transitions between different fidelity levels during training, allocating high computational resources to critical learning phases and reducing fidelity during exploration or plateau periods, thereby optimizing the accuracy-cost tradeoff.
3Reliability
If training sessions are extended to achieve better convergence, then model performance is improved, but training time and resource consumption increase
Solution Approach 1:
The system implements continuous feedback monitoring of learning convergence metrics, performance improvements, and resource consumption rates. Based on this feedback, the system dynamically adjusts training duration, resource allocation, and simulation parameters to achieve optimal convergence without unnecessary extended training, preventing waste of time and resources on diminishing returns.
Solution Approach 2:
Training session duration and intensity are dynamically adapted based on real-time assessment of convergence progress. The system extends training when performance improvements are significant and continues within budget, but reduces or terminates training when convergence plateaus are detected or budget constraints are approached, optimizing the time-performance tradeoff.
4Productivity
If more computational assets are dedicated to training, then learning speed is improved, but resource availability for other tasks decreases
Solution Approach 1:
The patent implements dynamic resource allocation that adjusts computational asset dedication based on training stage, budget constraints, and organizational priorities. The system transitions from static full-dedication to adaptive allocation, allowing resource sharing and flexibility to serve multiple objectives while maintaining effective learning speed during critical training phases.
Solution Approach 2:
The system applies partial resource dedication by allocating computational assets proportionally based on training needs and organizational requirements rather than full dedication. This allows simultaneous support for multiple training sessions or other computational tasks while maintaining sufficient resources for achieving learning objectives within budget constraints.
Data Source
AI summary
Reinforcement learning enables a framework of information technology assets that include software elements, computational hardware assets, and/or, bundled software and computational hardware systems and products. The performance of successive sessions of an inner loop reinforcement learning is directed and monitored by an outer loop reinforcement learning wherein the outer loop reinforcement learning is designed to reduce financial costs and computational asset requirements and/or optimize learning time in successive instantiations of inner loop reinforcement learning training sessions. The framework enables consideration of the license costs of domain specific simulators, the usage cost of hardware platforms, and the progress of a particular reinforcement learning training. The framework further enables reductions of these costs to orchestrate and train a neural network under budget constraints with respect to the available hardware and software licenses available at runtime. These improvements and optimizations may be performed by using heuristics and neural network algorithms.