Transfer Reinforcement Learning Shared Parameter Sets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current reinforcement learning methods for robots require restarting training for new tasks, leading to low learning efficiency and poor performance due to the lack of consideration for the relevance between previously learned and new tasks.
Innovation Solution
The proposed transfer reinforcement learning method determines a shared parameter set from previously learned tasks and applies it to new tasks, allowing for efficient learning by leveraging the relevance between tasks, thereby reducing the need for retraining from scratch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-task reinforcement learning is used to train multiple tasks, then learning efficiency for multiple tasks is improved, but learning efficiency for new tasks deteriorates because training must be restarted from scratch
Solution Approach 1:
The patent performs preliminary multi-task reinforcement learning to obtain a shared parameter set before encountering new tasks. This pre-learned shared parameter set is then transferred to accelerate learning of new tasks, avoiding complete retraining from scratch and reducing the time loss for new task acquisition.
Solution Approach 2:
The patent changes the parameter representation by decomposing task parameters into shared and task-specific components. The shared parameter set captures commonalities across tasks, while task-specific parameters handle unique aspects. This parameter transformation enables efficient transfer learning for new tasks by reusing the shared component.
2Measurement precision
If individual task training is performed for each task, then learning accuracy for each task is improved, but overall learning efficiency deteriorates due to redundant training
Solution Approach 1:
The patent merges multiple task parameters into a unified representation consisting of a shared parameter set and task-specific parameter sets. This merging eliminates redundant training by capturing common patterns in the shared parameters while preserving task-specific characteristics, thereby improving both accuracy and efficiency.
Solution Approach 2:
The shared parameter set serves as a universal foundation that can be applied across multiple tasks and new tasks. This multi-functional parameter set improves overall learning efficiency by avoiding redundant learning of common patterns, while task-specific parameters ensure accurate performance for each individual task.
3Loss of time
If transfer reinforcement learning is implemented using shared parameter sets, then learning efficiency for new tasks is improved, but system complexity increases due to parameter decomposition and matching
Solution Approach 1:
The patent segments task parameters into distinct shared and task-specific components. This segmentation simplifies the management of complexity by organizing parameters hierarchically, where the shared parameter set handles commonalities and task-specific sets handle variations, making the system more manageable despite the increased functionality.
Data Source
AI summary
Disclosed in the present disclosure are a transfer reinforcement learning method and apparatus, multi-task reinforcement learning method and apparatus, relating to the field of intelligent control technology. The transfer reinforcement learning method includes determining operational instructions for instructing an agent to perform a first task; determining an inclusion relation between multiple second tasks and the first tasks based on the operational instructions; determining a shared parameter set corresponding to the multiple second tasks based on the inclusion relation between the multiple second tasks and the first task, wherein the shared parameter set includes a plurality of parameters shared by the multiple second tasks; and performing transfer reinforcement learning based on the shared parameter set and the first task to obtain model parameters of a target policy model corresponding to the first task.