Hierarchical State Generation for RL Agent Learning
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Solution Overview
Problem
Virtual environments face challenges in efficiently identifying and implementing hierarchical states that balance generalizability with computational efficiency, as narrowly considering subsequent states/actions reduces options while broadly considering them becomes computationally burdensome.
Innovation Solution
A method and apparatus that receive inputs from agent interactions in a virtual environment, identify state sequences, and generate hierarchical states with associated actions, setting transition values based on value combinations and maximum values to optimize transitions and reduce computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If broadly considering subsequent states/actions in a virtual environment, then generalizability of options discovered is improved, but computational burden increases
Solution Approach 1:
The patent segments the state space by introducing hierarchical states that group multiple primitive states into higher-level abstractions. This segmentation allows the agent to reason about groups of states rather than individual states, improving generalizability while reducing computational burden by operating at multiple levels of abstraction simultaneously.
Solution Approach 2:
The patent adds a hierarchical dimension to the state representation, transforming a flat state space into a multi-level hierarchy. This dimensional change enables the system to capture general patterns across multiple primitive states while maintaining computational efficiency by processing information at appropriate hierarchical levels.
2Productivity
If narrowly considering subsequent states/actions in a virtual environment, then computational efficiency is improved, but generalizability of options discovered deteriorates
Solution Approach 1:
By segmenting the state space into hierarchical levels, the system can focus computational resources on salient higher-level states while implicitly capturing patterns across multiple primitive states. This segmentation maintains computational efficiency by reducing the effective state space size while preserving generalizability through hierarchical abstractions.
Solution Approach 2:
The hierarchical dimension enables the system to achieve computational efficiency by operating at higher levels of abstraction where state transitions are more general, while still maintaining the ability to reason about specific primitive states when needed, thus balancing efficiency and generalizability.
Data Source
AI summary
Reinforcement learning can be applied to generate hierarchical states. Inputs associated with interactions of an agent with an environment are received, where interactions include states and actions that cause state changes. An indication of a target state to achieve is received. A sequence including a first, second, and third state is identified, where the agent can perform a first action to transition from the first to second state, and a second action to transition from the second state to the third state. A hierarchical state can be generated, where a third action transitions from the first state to the hierarchical state, and a fourth action transactions from the hierarchical state to the third state.