Temporal Difference Scaling for Reinforcement Learning Stability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Reinforcement learning systems face challenges in managing varying reward scales, leading to instability, excessive computational resources, and slow learning due to unreliable techniques and hyper-parameter tuning requirements.
Innovation Solution
A method for training reinforcement learning neural networks using temporal difference errors scaled by a calculated scale factor, based on reward and return statistics, to stabilize learning and balance reward values across different tasks and stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning systems use varying reward scales across different tasks and stages, then the system can handle diverse complex tasks, but learning stability deteriorates and computational resources are excessively consumed
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the reward scale through a learned scaling factor that modifies the temporal difference error. This scaling factor is updated during training based on the observed reward variance, allowing the system to adapt to different reward magnitudes across tasks while maintaining stable learning dynamics. The transformation of the reward parameter through scaling resolves the contradiction between handling diverse tasks and maintaining learning stability.
2Reliability
If existing reward scaling techniques are used, then some tasks can be managed, but the techniques are unreliable and require extensive hyper-parameter tuning
Solution Approach 1:
The patent implements self-service by having the system automatically determine the optimal reward scaling factor through learning, eliminating the need for manual hyper-parameter tuning. The scaling factor is computed dynamically based on the statistical properties of the rewards observed during training, allowing the system to self-adjust to different tasks without requiring external configuration or extensive tuning experiments.
Solution Approach 2:
The patent uses feedback mechanisms where the scaling factor is continuously updated based on the observed performance and reward variance. The system monitors the temporal difference errors and adjusts the scaling factor accordingly, creating a closed-loop control system that improves reliability by adapting to the actual learning dynamics rather than relying on pre-set hyper-parameters.
3Power
If reinforcement learning systems process large rewards, then important tasks can be emphasized, but other rewards are dominated and learning is inhibited
Solution Approach 1:
The patent applies parameter changes by transforming the reward scale through a learned scaling factor that normalizes the magnitude of rewards. This transformation prevents large rewards from dominating the learning signal by adjusting them to a comparable scale with smaller rewards, allowing all rewards to contribute effectively to the learning process without any single reward type inhibiting overall learning productivity.
Data Source
AI summary
A reinforcement learning neural network system configured to manage rewards on scales that can vary significantly. The system determines the value of a scale factor that is applied to a temporal difference error used for reinforcement learning. The scale factor depends at least upon a variance of the rewards received during the reinforcement learning.