World Graph Learning for Sample-Efficient Hierarchical Reinforcement
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Solution Overview
Problem
Machine learning systems face challenges in acquiring and effectively applying world knowledge to solve tasks in complex environments, such as navigating and performing tasks in unknown environments, due to the difficulty in understanding the high-level structure of their operational environment.
Innovation Solution
A two-stage framework for learning world graphs to accelerate hierarchical reinforcement learning, which includes unsupervised world graph discovery using a novel recurrent differentiable binary latent model and a curiosity-driven goal-conditioned policy, and integrating this graph into a hierarchical reinforcement learning scheme with a Wide-then-Narrow instruction for efficient task solving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hierarchical reinforcement learning is used to solve tasks in complex environments, then task solving capability is improved, but sample efficiency deteriorates
Solution Approach 1:
The patent applies preliminary action by performing unsupervised world graph discovery before hierarchical reinforcement learning. The system pre-processes environment data to construct a world graph that captures high-level structure, which is then integrated into the HRL framework. This preliminary structuring of knowledge allows the HRL agent to leverage pre-computed environmental understanding, reducing the samples needed during actual task execution while maintaining or improving task-solving capability.
2Ease of operation
If world knowledge is acquired to understand high-level structure of environment, then navigation capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces a world graph as an intermediary structure that mediates between raw environment data and the reinforcement learning agent. The unsupervised world graph discovery module acts as an intermediary that processes environment data and produces a structured representation. This intermediary captures high-level environmental structure without requiring the main HRL system to directly process raw sensor data, thereby improving navigation capability while managing system complexity through modular architecture.
Data Source
AI summary
Systems and methods are provided for learning world graphs to accelerate hierarchical reinforcement learning (HRL) for the training of a machine learning system. The systems and methods employ or implement a two-stage framework or approach that includes (1) unsupervised world graph discovery, and (2) accelerated hierarchical reinforcement learning by integrating the graph.