/Offline Primitive Discovery For Accelerating Data-driven Reinforcement Learning
Abstract

A method includes determining a first state associated with a particular task, and determining, by a task policy model, a latent space representation of the first state. The task policy model may have been trained to define, for each respective state of a plurality of possible states associated with the particular task, a corresponding latent space representation of the respective state. The method also includes determining, by a primitive policy model and based on the first state and the latent space representation of the first state, an action to take as part of the particular task. The primitive policy model may have been trained to define a space of primitive policies for the plurality of possible states associated with the particular task and a plurality of possible latent space representations. The method further includes executing the action to reach a second state associated with the particular task.

Full Text

What is claimed is:

A method includes determining a first state associated with a particular task, and determining, by a task policy model, a latent space representation of the first state. The task policy model may have been trained to define, for each respective state of a plurality of possible states associated with the particular task, a corresponding latent space representation of the respective state. The method also includes determining, by a primitive policy model and based on the first state and the latent space representation of the first state, an action to take as part of the particular task. The primitive policy model may have been trained to define a space of primitive policies for the plurality of possible states associated with the particular task and a plurality of possible latent space representations. The method further includes executing the action to reach a second state associated with the particular task.
Timeline
Filed
06/08/2026
Published
09/24/2026
Granted
Not Available
IPC Codes(2)
G06N 3/0455:Auto-encoder networks; Encoder-decoder networks
G06N 3/092:Reinforcement learning