Unsupervised RL Goal Detection via Discriminative Feature Partitioning
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Solution Overview
Problem
Reinforcement learning systems face challenges in effectively partitioning complex tasks into subtasks and generating suitable reward functions for training agents, especially when only a small number of demonstration sequences are available, as existing methods require costly labeling and explicit sub-goal specification.
Innovation Solution
A system that processes demonstration sequences to determine discriminative features for subtasks, allowing for unsupervised partitioning and generation of dense, smooth reward functions, which can be used to train reinforcement learning agents efficiently without explicit labeling or sub-goal specification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit sub-goal specification and costly labeling are used, then the accuracy of task partitioning and reward function generation is improved, but the time consumption and resource costs increase significantly
Solution Approach 1:
The system performs unsupervised task partitioning by automatically analyzing demonstration sequences without requiring external labeling. The algorithm self-organizes the partitioning based on intrinsic patterns in the data, eliminating the need for costly manual annotation while maintaining partitioning quality
Solution Approach 2:
The patent introduces intermediate representations (latent variables and feature extractors) that bridge the gap between raw demonstration sequences and task partitioning. These intermediaries enable the system to derive meaningful structure from unlabeled data without direct human intervention
2Productivity
If a small number of demonstration sequences are used, then the data processing time and resource requirements are reduced, but the quality of learned features and reward functions may deteriorate
Solution Approach 1:
The system transforms the problem by changing parameters in the feature space through learned representations. By projecting demonstrations into latent spaces and learning discriminative features in this transformed space, the system can extract maximal information from limited data, maintaining feature quality even with few demonstrations
Solution Approach 2:
The patent segments the learning process into distinct components: feature extraction, task partitioning, and reward generation. This segmentation allows each component to be optimized independently, enabling effective learning from limited demonstrations by focusing computational resources on extracting maximal discriminative information
3Ease of manufacture
If unsupervised partitioning is implemented, then the labeling costs and manual intervention are reduced, but the complexity of the algorithm increases
Solution Approach 1:
The patent replaces manual labeling mechanisms with automated algorithmic partitioning. By substituting human expert annotation with machine learning-based unsupervised partitioning, the system eliminates labeling costs while the algorithmic complexity is managed through efficient feature extraction and latent variable modeling
4Productivity
If dense and smooth reward functions are generated from few demonstrations, then the training performance of the agent is improved, but the requirement for precise feature discrimination increases
Solution Approach 1:
The system performs preliminary feature extraction and task partitioning before reward function generation. By pre-processing the demonstration sequences to extract discriminative features and establish task structure in advance, the system prepares high-quality inputs for reward generation, enabling dense and smooth reward functions that effectively guide agent training
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting intermediate reinforcement learning goals. One of the methods includes obtaining a plurality of demonstration sequences, each of the demonstration sequences being a sequence of images of an environment while a respective instance of a reinforcement learning task is being performed; for each demonstration sequence, processing each image in the demonstration sequence through an image processing neural network to determine feature values for a respective set of features for the image; determining, from the demonstration sequences, a partitioning of the reinforcement learning task into a plurality of subtasks, wherein each image in each demonstration sequence is assigned to a respective subtask of the plurality of subtasks; and determining, from the feature values for the images in the demonstration sequences, a respective set of discriminative features for each of the plurality of subtasks.