State-Augmented Reinforcement Learning via Classifier Embeddings
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Solution Overview
Problem
Machine learning systems face challenges in handling heterogeneous and non-stationary data, particularly in reinforcement learning applications like portfolio management, due to data heterogeneity and environment uncertainty, which affects the accuracy and robustness of decision-making processes.
Innovation Solution
The implementation of a state-augmented reinforcement learning (SARL) framework that incorporates embeddings from machine learning classifiers to create augmented states, combining diverse data sources such as asset prices and news articles, to enhance the learning process and improve decision-making in reinforcement learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reinforcement learning models use only observable state data, then the model simplicity is maintained, but the accuracy and robustness of predictions deteriorate due to data heterogeneity and environment uncertainty
Solution Approach 1:
The patent embeds embeddings from multiple machine learning classifiers (trained on different data sources such as asset prices, news articles, and social media) within the reinforcement learning state representation. This nesting approach allows the RL model to access rich, multi-source information without restructuring the entire system, thereby improving prediction accuracy while maintaining manageable complexity.
Solution Approach 2:
The patent introduces embeddings as intermediary representations that translate heterogeneous data from different sources into a unified format compatible with reinforcement learning. These embeddings act as mediators between diverse data sources and the RL decision-making process, enabling accurate integration of multiple information types without directly increasing RL model complexity.
2Reliability
If multiple diverse data sources are integrated into reinforcement learning, then the robustness to environment uncertainty improves, but the difficulty of handling heterogeneous data increases
Solution Approach 1:
The patent transforms heterogeneous data from different sources into a unified embedding space by changing the parameter representation of each data type. Each machine learning classifier is trained to produce embeddings with consistent dimensional and statistical properties, enabling seamless integration of diverse data sources like prices, news, and social media into the reinforcement learning framework.
3Loss of information
If embeddings from machine learning classifiers are extracted and integrated, then the information completeness of the state increases, but the computational resources required increase
Solution Approach 1:
The patent performs preliminary processing by training machine learning classifiers offline to extract embeddings from diverse data sources before feeding them into the reinforcement learning model. This preliminary action allows complex information extraction to be done in advance, reducing the real-time computational burden during RL decision-making while maintaining complete information representation.
Data Source
AI summary
A processor training a reinforcement learning model can include receiving a first dataset representing an observable state in reinforcement learning to train a machine to perform an action. The processor receives a second dataset. Using the second dataset, the processor trains a machine learning classifier to make a prediction about an entity related to the action. The processor extracts an embedding from the trained machine learning classifier, and augments the observable state with the embedding to create an augmented state. Based on the augmented state, the processor trains a reinforcement learning model to learn a policy for performing the action, the policy including a mapping from state space to action space.