RL Attention-Guided Concept Extraction for Image-Based Agents
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Solution Overview
Problem
Existing approaches to concept extraction are limited to neural networks used for classifying discrete categories and are not applicable to reinforcement learning networks, which lack a clear target class for each image.
Innovation Solution
A system for unsupervised concept extraction from reinforcement learning agents that trains the agent on an image processing task, extracts a dataset of states and actions, applies attention measures to the input space, extracts image patches, clusters inputs based on similarity, and provides non-semantic concept labels as distances to cluster centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing concept extraction approaches are used, then classification performance is improved, but applicability to reinforcement learning networks is lost
Solution Approach 1:
Instead of adapting concept extraction to work with reinforcement learning outputs, the patent inverts the approach by using reinforcement learning attention mechanisms to extract concepts from input images directly. The attention maps from the RL agent are used to guide concept extraction from the visual input space, rather than trying to extract concepts from action outputs.
Solution Approach 2:
The patent introduces attention maps as an intermediary between the reinforcement learning agent and the concept extraction process. These attention maps serve as a bridge that connects the RL agent's decision-making process with the input image features, enabling concept extraction without requiring discrete class labels.
2Productivity
If reinforcement learning agents are trained on image processing tasks, then task performance is improved, but concept extraction capability deteriorates
Solution Approach 1:
The patent extracts concepts from the trained RL agent's attention mechanisms before deploying the agent for task execution. By performing concept extraction as a preliminary analysis of the trained agent's behavior patterns, we preserve both the task performance of the RL agent and gain additional concept-level insights without retraining.
Solution Approach 2:
The extracted concepts provide feedback about the RL agent's decision-making processes and attention patterns. This feedback can be used to interpret agent behavior, debug performance issues, and potentially guide further training or architecture improvements without compromising the original task performance.
Data Source
AI summary
Described is a method for improved performance of agent-based machine learning. The method includes training a reinforcement learning (RL) agent on an image processing task. A dataset of states and corresponding actions is then extracted from the RL agent. A measure of attention is applied to an input space of the RL agent. During action selection by the RL agent, image patches of the input space are extracted based on the applied measure of attention. Portions of a set of inputs are clustered based on similarity to the image patches, generating a set of clusters having cluster centers. Non-semantic concept labels are provided as distances to the cluster centers for each state in the dataset.