Reinforcement Learning Model Retraining for Text Response Generalization
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Solution Overview
Problem
Text-based response environments, such as games and voice assistants, face challenges due to partially observable state spaces and large action spaces, leading to models becoming overfit to specific environments, which hinders performance in new unseen environments.
Innovation Solution
A reinforcement learning-based method that retrains models by emphasizing relevant words in state representations, using a ground-truth action representation to modify state representations, and iteratively updating the model to produce more accurate action selections, thereby reducing overfitting and improving generalization across environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a model is trained using reinforcement learning in text-based response environments, then the model can learn action selection policies, but the model becomes overfit to specific environments and fails to generalize to new unseen environments
Solution Approach 1:
The patent changes the parameter representation of state by emphasizing specific words through word embeddings. The word embedding layer transforms discrete word tokens into continuous vector representations, and the emphasis mechanism adjusts the weights of different words in the state representation. This parameter transformation allows the model to focus on critical features while maintaining generalization capability across different environments.
Solution Approach 2:
The patent applies local quality by emphasizing specific words within the state representation rather than treating all words equally. The word emphasis mechanism assigns different weights to different words based on their importance, allowing the model to pay selective attention to relevant features. This localized emphasis helps the model generalize better by focusing on invariant features across environments.
2Loss of information
If the model processes complete state representations including all words, then comprehensive information is available for action selection, but unnecessary words increase computational complexity and hinder generalization
Solution Approach 1:
The patent extracts essential information from the complete state representation by emphasizing only the relevant words. The word embedding mechanism selectively highlights important words while downweighting or ignoring unnecessary words. This extraction process reduces the effective dimensionality of the input space without losing critical information needed for action selection.
Solution Approach 2:
The patent segments the state representation into individual word tokens that can be independently processed and weighted. By breaking down the complete state into discrete word units, the model can apply differential emphasis to each word, processing only the essential portions in full detail while reducing attention to less important elements.
Data Source
AI summary
In an approach, a processor trains a model, via a reinforcement learning process, to produce a first action function for relating states of a natural language based response environment to actions applicable to the natural language based response environment. A processor retrains the model, via the reinforcement learning process, to produce a second action function, including iterations of: applying the first action function to a current state representation of the natural language based response environment to obtain a ground-truth action representation, emphasizing a word of the current state representation based on relevancy to the ground-truth action representation to obtain a modified state representation, applying a model to the modified state representation to obtain an untrained action representation, and submitting the untrained action representation to a natural language based response environment to obtain a subsequent state representation, where the subsequent state representation becomes the current state representation for a subsequent iteration.