Multi-Task Reinforcement Learning Policy Parameter Subspace
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Solution Overview
Problem
Current multi-task reinforcement learning methods face challenges in determining what to share and how to share parameters among tasks, leading to inefficiencies and difficulties in training effective policies for robots performing diverse tasks.
Innovation Solution
The Parameter-Compositional (PaCo) approach learns a task-agnostic parameter set forming a subspace in the policy parameter space, allowing task-specific policies to be inferred using compositional vectors, enabling direct composition of policies in the parameter space rather than the action space, which unifies previous sharing structures and stabilizes multi-task reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all parameters are shared among tasks with the same robot but different goals, then the policy can be made goal-conditioned and applied to multiple tasks, but additional difficulties arise for uncorrelated skills and tasks with different skills
Solution Approach 1:
The patent segments the policy parameters into task-agnostic components and task-specific components. The task-agnostic parameters form a shared subspace that is common across all tasks, while task-specific parameters capture the unique characteristics of each skill. This segmentation allows the model to share common representations while maintaining task-specific capabilities, resolving the contradiction between versatility and complexity.
Solution Approach 2:
The patent introduces a new dimension to the parameter space by forming a subspace of task-agnostic parameters. Instead of sharing all parameters directly, the model creates a lower-dimensional shared subspace that captures commonalities across tasks. This dimensional transformation enables efficient parameter sharing while accommodating task-specific variations through additional dimensions.
2Productivity
If parameters are shared to improve efficiency in number of parameters and roll-out environment steps, then training multiple tasks together becomes more efficient, but conflicts arise between tasks with different skills
Solution Approach 1:
By segmenting parameters into shared task-agnostic components and task-specific components, the model can train multiple tasks together efficiently while maintaining task performance stability. The shared subspace provides common representations that improve training efficiency, while the task-specific components prevent conflicts by allowing each task to have its own specialized parameters.
Solution Approach 2:
The patent applies local quality by allowing different parts of the parameter space to have different properties. The task-agnostic subspace provides generalizable representations with high efficiency, while task-specific parameters provide specialized representations with high reliability for each individual task. This local differentiation resolves the contradiction between training efficiency and performance stability.
Data Source
AI summary
Disclosed in the present disclosure are a method for training a multi-task model through multi-task reinforcement learning, an apparatus, an electronic device and a non-transitory computer readable storage medium. A method for training a multi-task model through multi-task reinforcement learning, including: acquiring observation signals observed for an environment by an agent; receiving T instructions each for instructing the agent to perform one of T tasks, T being a preset positive integer greater than 1; and generating K base policy models by performing training through multi-task reinforcement learning over a neural network based on the observation signals and the T instructions, wherein the K base policy models are combinable for generating respective task policy models for the T tasks to obtain the multi-task model for achieving the T tasks.