18439222. Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning simplified abstract (The Regents of the University of California)
Contents
Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning
Organization Name
The Regents of the University of California
Inventor(s)
Masayoshi Tomizuka of Berkeley CA (US)
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.
