Game Environment Generation from Unstructured Text for Reinforcement Learning
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Solution Overview
Problem
Current reinforcement learning frameworks require manually structured game environments, which are not scalable for real-world decision-making problems, limiting their application in generating environments from unstructured textual data.
Innovation Solution
A computer-implemented method and system that generates game environments from unstructured textual corpus by parsing natural language data to extract representative features, characterizing state and action properties, and bootstrapping a computer environment using a state transition matrix, leveraging natural language processing and unsupervised machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually structured game environments are used for reinforcement learning, then the environment structure is well-defined and reliable, but the method is not scalable for real-world decision-making problems
Solution Approach 1:
The system automatically generates structured game environments from unstructured text records using natural language processing and machine learning algorithms. The environment generation process is self-service, requiring no manual structuring by domain experts, thus achieving scalability while maintaining reliability through automated feature extraction and state-space construction
Solution Approach 2:
The patent replaces the manual mechanical process of environment structuring with an automated computational system. Natural language processing algorithms and machine learning models substitute the manual work of defining state spaces and action spaces, enabling scalable generation of environments from text records while preserving the structural integrity needed for reinforcement learning
2Productivity
If unstructured text records are transformed into structured environments, then scalability is improved, but the complexity of processing and interpreting text data increases
Solution Approach 1:
The text processing pipeline is segmented into distinct modular stages: text corpus ingestion, feature extraction, state identification, action identification, and environment construction. Each stage processes specific aspects of the text data independently, reducing overall complexity while enabling scalable transformation from unstructured text to structured reinforcement learning environments
Solution Approach 2:
The patent introduces intermediate representations such as feature vectors and state-action mappings that bridge unstructured text and structured environments. These intermediaries simplify the transformation process by providing structured formats for processing text data, reducing complexity while maintaining scalability in environment generation
Data Source
AI summary
Representative features representing actions and states described in text corpus comprising unstructured natural language data are generated. State properties associated with the states from the representative features and action properties associated with the actions from the representative features are generated. A state transition matrix with initial configurations is generated based on the state space and action space properties. A computer environment is bootstrapped based on the state space, the action space and the initial state transition matrix.