Hierarchical Reinforcement Learning for Off-Policy Robotic Control
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Solution Overview
Problem
Existing robotic control systems, particularly those using deep reinforcement learning, are limited to atomic tasks and lack the capability for complex multi-level reasoning involving locomotion behaviors, object interaction, and discrete decision-making.
Innovation Solution
A multi-level hierarchical reinforcement learning (HRL) model is employed, comprising a higher-level policy model and a lower-level policy model, where the higher-level model generates goals over extended time scales, and the lower-level model generates direct control commands, with off-policy training and correction techniques to enhance efficiency and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep reinforcement learning is used for robotic control, then the robot can perform atomic control tasks, but it cannot perform complex multi-level reasoning tasks involving locomotion behaviors and object manipulation
Solution Approach 1:
The control architecture is segmented into hierarchical levels: high-level policy networks generate abstract goals and subtasks, while low-level policy networks execute specific actions. This segmentation enables complex task handling by breaking down reasoning into manageable hierarchical stages without requiring a monolithic complex controller.
Solution Approach 2:
Goal states serve as intermediaries between high-level reasoning and low-level execution. The high-level policy generates goal states that mediate the translation of abstract task intentions into concrete actionable commands for the low-level policy, enabling multi-level reasoning without direct coupling between planning and execution.
2Reliability
If the lower-level policy model is trained using traditional on-policy methods, then it can learn effective policies, but it requires large amounts of experience data and computational resources
Solution Approach 1:
The high-level policy is trained first to generate goal states and subtasks before training the low-level policy. This preliminary action provides structured training targets for the low-level policy, enabling more efficient learning with fewer experience samples and reduced computational resource consumption during the low-level training phase.
Data Source
AI summary
Training and/or utilizing a hierarchical reinforcement learning (HRL) model for robotic control. The HRL model can include at least a higher-level policy model and a lower-level policy model. Some implementations relate to technique(s) that enable more efficient off-policy training to be utilized in training of the higher-level policy model and/or the lower-level policy model. Some of those implementations utilize off-policy correction, which re-labels higher-level actions of experience data, generated in the past utilizing a previously trained version of the HRL model, with modified higher-level actions. The modified higher-level actions are then utilized to off-policy train the higher-level policy model. This can enable effective off-policy training despite the lower-level policy model being a different version at training time (relative to the version when the experience data was collected).