Multi-Objective Reward Neural Networks for Reinforcement Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural network training methods are inefficient and computationally expensive, particularly when multiple objectives with different loss functions are involved, requiring multiple passes and significant resource consumption.
Innovation Solution
A multi-objective reward neural network that generates multiple reward scores in a single forward pass, incorporating both point-wise and pair-wise objectives, allowing for efficient training without additional computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate forward passes are performed to generate reward scores for different objectives, then comprehensive multi-objective evaluation is achieved, but computational overhead and training time increase significantly
Solution Approach 1:
The patent combines multiple separate reward evaluation processes into a single unified forward pass through the neural network. The multi-objective reward neural network simultaneously generates reward scores for multiple different objectives (e.g., accuracy, fairness, robustness) in one computational pass, eliminating the need for multiple sequential evaluations and reducing computational overhead.
Solution Approach 2:
The reward neural network is designed with multi-functionality to handle multiple objectives simultaneously. A single neural network structure performs multiple evaluation functions by generating different reward scores for different objectives through shared computational layers, making the system universal rather than requiring separate specialized evaluators for each objective.
2Reliability
If multiple separate training runs are performed for different loss functions, then each objective is optimized thoroughly, but computational resources and training time are consumed repeatedly
Solution Approach 1:
The patent merges multiple separate training processes into a single unified training run. The multi-objective reward neural network computes gradients for multiple objectives simultaneously during one training pass, allowing all objectives to be optimized together rather than requiring separate training runs for each loss function, thereby reducing computational resource consumption.
Solution Approach 2:
The training process maintains continuity by performing all objective optimizations in an uninterrupted single pass. The neural network continuously computes rewards and gradients for multiple objectives simultaneously without pausing or restarting between objectives, ensuring that training action is continuous and efficient rather than fragmented across multiple separate runs.
3Adaptability or versatility
If separate reward models are trained for point-wise and pair-wise objectives, then each objective type is handled specifically, but device complexity and training overhead increase
Solution Approach 1:
The patent employs a universal reward neural network that can handle both point-wise and pair-wise objectives through its multi-functional design. The same neural network architecture processes different objective types by generating appropriate reward scores based on the input type, eliminating the need for separate specialized models and reducing overall system complexity.
Solution Approach 2:
The neural network internally segments different objective types through separate processing pathways or loss function computations while maintaining a unified architecture. The model divides the computation into distinct segments for handling point-wise versus pair-wise objectives, allowing specific treatment of each objective type within the overall unified framework without requiring separate external models.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network through reinforcement learning. One of the methods includes, at each of a plurality of training steps: obtaining one or more training network inputs for the training step; processing the training network inputs using the neural network to generate one or more training network outputs for each of the training network inputs; for each training network output, processing a reward input comprising the training network output using a multi-objective reward neural network to generate a respective reward score for each of a plurality of objectives; for each training network output, generating, from the respective reward scores for each of the plurality of objectives, a combined reward score; and training the neural network through reinforcement learning using the combined reward scores for the training network outputs.