18673510. DATA-EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING simplified abstract (Google LLC)
Contents
DATA-EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING
Organization Name
Inventor(s)
Honglak Lee of Mountain View CA (US)
Shixiang Gu of Mountain View CA (US)
Sergey Levine of Berkeley CA (US)
DATA-EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18673510 titled 'DATA-EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING
Abstract: 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 techniques 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).
Key Features and Innovation:
- Hierarchical reinforcement learning (HRL) model for robotic control
- Higher-level policy model and lower-level policy model
- Off-policy training techniques for more efficiency
- Off-policy correction for re-labeling higher-level actions
- Effective off-policy training despite version differences
Potential Applications: - Robotics - Autonomous systems - Industrial automation - Process control
Problems Solved: - Efficient training of hierarchical reinforcement learning models - Overcoming version differences in training data and policy models
Benefits: - Improved robotic control performance - Enhanced efficiency in training - Adaptability to different versions of policy models
Commercial Applications: Title: Advanced Robotic Control Systems Utilizing Hierarchical Reinforcement Learning Potential commercial uses include: - Manufacturing automation - Warehouse logistics - Autonomous vehicles - Healthcare robotics
Prior Art: Research in the field of reinforcement learning and robotic control systems can provide valuable insights into prior art related to this technology.
Frequently Updated Research: Stay updated on advancements in reinforcement learning algorithms, robotic control systems, and hierarchical reinforcement learning models for the latest developments in the field.
Questions about Hierarchical Reinforcement Learning: 1. How does hierarchical reinforcement learning differ from traditional reinforcement learning methods? Hierarchical reinforcement learning involves learning multiple levels of policies, allowing for more complex decision-making compared to traditional reinforcement learning.
2. What are the advantages of utilizing off-policy training in hierarchical reinforcement learning models? Off-policy training enables more efficient learning by utilizing past experience data, even when the policy models have been updated.
Original Abstract Submitted
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).
