Difference Graph Common Sense Extraction for RL Action Selection
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Solution Overview
Problem
Autonomous agents struggle with efficient next step decision making due to challenges in constructing knowledge bases and determining appropriate responses in environments with partial observability, long-term dependencies, sparse reward signals, and large action spaces, particularly in text-based games.
Innovation Solution
The implementation of a Difference Graph Reinforcement Learning (DiffG-RL) mechanism that constructs a difference graph from observed environment states and common sense, using a dedicated graph encoder to extract relevant common sense, and a framework for selecting actions based on these differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous agents use traditional reinforcement learning approaches, then they can learn from environment interactions, but sample efficiency is poor due to large action spaces and sparse reward signals
Solution Approach 1:
The system performs preliminary action by constructing a difference graph before reinforcement learning to pre-identify relevant common sense knowledge and potential actions. This pre-processing step organizes knowledge structures and filters the action space in advance, allowing the agent to start learning from a more focused state rather than exploring the entire large action space from scratch, thereby improving sample efficiency
Solution Approach 2:
The difference graph serves as an intermediary structure that mediates between the agent's current state and the reinforcement learning process. It extracts and organizes common sense knowledge relevant to the current situation, translating unstructured knowledge into a structured representation that guides action selection. This intermediary filters and prioritizes information, reducing the effective action space and improving sample efficiency without sacrificing decision-making accuracy
2Reliability
If autonomous agents construct comprehensive knowledge bases, then decision-making capabilities improve, but system complexity increases
Solution Approach 1:
The system applies extraction by pulling out only the relevant common sense knowledge needed for the current situation from the broader knowledge base. The difference graph extracts specific triples (subject, predicate, object) that are relevant to the current state and goal, rather than using the entire knowledge base. This selective extraction reduces the effective complexity of knowledge processing while maintaining decision-making capabilities
Solution Approach 2:
The knowledge base is segmented into situation-specific subsets through the difference graph construction process. Instead of processing a monolithic comprehensive knowledge base, the system divides knowledge into relevant fragments based on the current environment state and task requirements. This segmentation reduces computational complexity while preserving the necessary decision-making capabilities for each specific situation
3Measurement precision
If autonomous agents use all available common sense knowledge, then action accuracy improves, but processing efficiency decreases
Solution Approach 1:
The system applies local quality by making the common sense knowledge representation adaptive to each specific situation. The difference graph constructs a localized knowledge subset tailored to the current environment state and task requirements, rather than applying uniform processing to all common sense knowledge. This localized approach ensures high action selection accuracy for the current context while improving processing efficiency by excluding irrelevant knowledge
Solution Approach 2:
The system uses partial action by extracting and processing only the portion of common sense knowledge that is relevant to the current situation. The difference graph identifies and processes a subset of common sense triples that are sufficient for making accurate action decisions, rather than exhaustively processing all available common sense knowledge. This partial processing maintains action accuracy while significantly improving processing speed
Data Source
AI summary
Aspects of the invention include systems and methods configured to extract enriched target-oriented common sense from grounded graphs to support efficient next step decision making of an autonomous agent. A non-limiting example computer-implemented method includes extracting common sense from a source. The extracted common sense can include a first knowledge graph. An environment state can be extracted from an observation. The extracted environment state can include a second knowledge graph. The second knowledge graph can include an interactive object and a state of the interactive object. A difference graph including the extracted common sense and the extracted environment state can be generated. A next action is selected based on the difference graph and the next action is taken by an autonomous agent.