Hierarchical Reinforcement Learning for Complex Autonomous Behaviors
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Solution Overview
Problem
Current reinforcement learning systems are limited in their capacity for complex decision-making due to their reliance on machine learning models other than neural nets, which restricts their ability to handle intricate scenarios effectively.
Innovation Solution
A learning system utilizing hierarchical reinforcement learning with a high-level controller and a low-level controller, where the high-level controller uses a neural net to select behaviors based on observations and the low-level controller generates output commands for scripted actions, and additional neural nets are trained to determine the duration of high-level behaviors, employing softmax learning functions for probability calculations and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional machine learning models (non-neural net) are used for high-level behavior selection, then the system is simpler to implement, but the capacity for complex decision-making is limited
Solution Approach 1:
The system divides behavior selection into hierarchical levels: high-level behavior selection (strategic decisions) and low-level behavior execution (tactical actions). This segmentation allows neural nets to handle only the complex high-level decisions while simpler models handle execution, resolving the contradiction between complexity and decision-making capacity.
Solution Approach 2:
The patent introduces a hierarchical dimension to the decision-making process, adding a temporal and structural layer between high-level selection and low-level execution. This dimensional addition enables complex neural net processing at the high level without requiring the entire system to be complex, thus improving decision-making capacity while managing overall system complexity.
2Adaptability or versatility
If hierarchical reinforcement learning with neural nets is implemented, then complex autonomous behavior generation is enabled, but the device complexity increases
Solution Approach 1:
The controller architecture is segmented into distinct high-level and low-level components with clearly defined interfaces. The high-level controller handles behavior selection while the low-level controller handles execution, reducing architectural complexity through functional separation despite the advanced neural net capabilities.
Solution Approach 2:
The patent introduces an intermediary layer (the high-level behavior selection module) that mediates between the complex neural net processing and the simpler execution mechanisms. This intermediary simplifies the overall architecture by providing a clear interface and abstraction layer, reducing the complexity burden of implementing hierarchical reinforcement learning with neural nets.
3Adaptability or versatility
If frequent high-level behavior selection is performed, then adaptability to changing environments improves, but training time and computational resources increase
Solution Approach 1:
The system employs periodic action by selecting high-level behaviors at discrete time intervals rather than continuously. This periodic high-level selection combined with continuous low-level execution reduces computational burden during training while maintaining environmental adaptability, as the system only needs to re-evaluate high-level decisions at specific intervals rather than at every time step.
Data Source
AI summary
Described is a system for autonomous behavior generation. The system includes both a high-level controller and a low-level controller. The high-level controller receives observations from an environment and, using a neural net, selects a high-level behavior based on the observations from the environment. The low-level controller generates an output command for a scripted action based on the selected one high-level behavior. After generating the output command, the system can implement an action, such as causing a device to perform the scripted action.