View source for 18439222. Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning simplified abstract (The Regents of the University of California)

=Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning=


==Organization Name==

[[:Category:The Regents of the University of California|The Regents of the University of California]]


[[Category:The Regents of the University of California]]

==Inventor(s)==

[[:Category:Minglei Huang of Novi MI (US)|Minglei Huang of Novi MI (US)]][[Category:Minglei Huang of Novi MI (US)]]

[[:Category:Wei Zhan of Berkeley CA (US)|Wei Zhan of Berkeley CA (US)]][[Category:Wei Zhan of Berkeley CA (US)]]

[[:Category:Masayoshi Tomizuka of Berkeley CA (US)|Masayoshi Tomizuka of Berkeley CA (US)]][[Category:Masayoshi Tomizuka of Berkeley CA (US)]]

[[:Category:Chen Tang of Berkeley CA (US)|Chen Tang of Berkeley CA (US)]][[Category:Chen Tang of Berkeley CA (US)]]

[[:Category:Jinning Li of Berkeley CA (US)|Jinning Li of Berkeley CA (US)]][[Category:Jinning Li of Berkeley CA (US)]]

==Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18439222 titled 'Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning

'''Simplified Explanation''': 

The patent application describes a method and system for controlling a device by training a low-level policy and value function, training a high-level goal planner, obtaining device observations, and generating executable actions to operate the device.

* Training a low-level policy and value function using goal-conditioned episodes
* Training a high-level goal planner with high-level goals and sub-goals
* Maximizing cumulative reward over sub-goals for future time steps
* Generating executable actions based on observations and goals
* Operating the device with the generated actions

'''Key Features and Innovation''':

* Utilizes goal-conditioned episodes for training
* Integrates low-level policy and value function with high-level goal planner
* Maximizes cumulative reward over future time steps for goal achievement
* Enables efficient device control based on observations and goals

'''Potential Applications''':

* Robotics control systems
* Autonomous vehicles
* Industrial automation
* Smart home devices

'''Problems Solved''':

* Efficient device control based on high-level goals
* Seamless integration of low-level and high-level control strategies
* Optimization of actions for goal achievement

'''Benefits''':

* Enhanced device performance
* Improved goal achievement
* Streamlined control processes

'''Commercial Applications''''':

The technology can be applied in various industries such as robotics, autonomous vehicles, industrial automation, and smart home devices to optimize control processes and enhance performance.

'''Questions about the technology''':
1. How does the training process for the low-level policy and value function work?
2. What are the specific benefits of using goal-conditioned episodes for training?

'''Frequently Updated Research''''':

Stay updated on advancements in reinforcement learning algorithms and control systems to enhance the efficiency and effectiveness of the technology.


==Original Abstract Submitted==

A method and system for controlling a device includes training a low-level policy to form a trained low-level policy and a low-level value function to form a trained goal conditioned value function, wherein training is performed using a static data set using goal conditioned episodes, training a high-level goal planner having high level goals having high-level sub-goals corresponding to a plurality of future time steps using the low-level value function to maximize a cumulative reward over the sub-goals for the plurality of future time steps so that the sub-goals are reachable by the low-level policy, obtaining an observation of a device, and generating an executable action using the low-level policy and the high-level goal planner and operating the device with the executable action.



[[Category:B60W60/00]]
[[Category:G06V20/56]]
[[Category:CPC_B60W60/001]]