Google llc (20240308068). DATA-EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING simplified abstract
Contents
- 1 DATA-EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DATA-EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Hierarchical Reinforcement Learning
- 1.13 Original Abstract Submitted
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 20240308068 titled 'DATA-EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING
Simplified Explanation
The patent application discusses training and utilizing a hierarchical reinforcement learning (HRL) model for robotic control. The model consists of a higher-level policy model and a lower-level policy model. Techniques are proposed to enable more efficient off-policy training for both levels of the model.
Key Features and Innovation
- Hierarchical reinforcement learning (HRL) model for robotic control
- Higher-level policy model and lower-level policy model
- Techniques for efficient off-policy training
- Utilization of off-policy correction for training
- Re-labeling of higher-level actions for effective off-policy training
Potential Applications
The technology can be applied in various robotic control systems, autonomous vehicles, industrial automation, and other fields requiring complex decision-making processes.
Problems Solved
The technology addresses the challenge of training hierarchical reinforcement learning models efficiently, especially when the lower-level policy model is different at training time compared to when the experience data was collected.
Benefits
- Improved efficiency in training hierarchical reinforcement learning models
- Enhanced performance in robotic control systems
- Adaptability to changing environments and tasks
Commercial Applications
Title: Efficient Hierarchical Reinforcement Learning for Robotic Control The technology can be commercialized in industries such as manufacturing, logistics, healthcare, and agriculture for optimizing processes, enhancing productivity, and reducing human intervention.
Prior Art
Readers can explore prior research on hierarchical reinforcement learning, off-policy training, and robotic control systems to understand the existing knowledge in this field.
Frequently Updated Research
Researchers are continuously exploring advancements in hierarchical reinforcement learning, off-policy training techniques, and applications in robotic control to improve performance and scalability.
Questions about Hierarchical Reinforcement Learning
How does off-policy correction improve training efficiency in hierarchical reinforcement learning models?
Off-policy correction allows for re-labeling of higher-level actions in experience data, enabling effective training despite differences in the lower-level policy model.
What are the potential challenges in implementing a hierarchical reinforcement learning model in real-world robotic systems?
Challenges may include scalability, computational complexity, and the need for robust adaptation to dynamic environments.
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).
