Dispatcher-Executor Neural Network Segmentation for Multi-Task Learning
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Solution Overview
Problem
Existing neural network-based control systems struggle to generalize tasks effectively, requiring extensive training data and failing to perform new tasks without specific training.
Innovation Solution
The system partitions the controller architecture into a dispatcher and an executor, using a control channel to structure this partitioning. The dispatcher understands the environment and generates executor instructions, while the executor controls the agent using these instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a monolithic neural network controller is used, then the system can be trained on specific tasks, but it fails to generalize to new tasks without extensive retraining
Solution Approach 1:
The controller is segmented into two distinct neural networks: a dispatcher network that processes task descriptions and generates executor instructions, and an executor network that receives these instructions and controls the agent. This segmentation allows the executor to learn task-agnostic skills while the dispatcher learns to translate high-level task descriptions into executor instructions, enabling better generalization to new tasks without requiring extensive retraining of the entire system.
2Productivity
If a monolithic neural network controller is used, then the architecture is simple, but the system requires extensive training resources and computing power
Solution Approach 1:
By dividing the controller into dispatcher and executor components, the system can train each network separately on specific aspects of task performance. The executor can be trained on skill execution while the dispatcher is trained on task interpretation, reducing the overall training burden and computing resources required compared to training a single large monolithic network on all aspects simultaneously.
Solution Approach 2:
The executor network is designed to be universal and task-agnostic, capable of executing multiple different skills and sub-tasks. This multi-functionality allows the same executor architecture to handle various tasks by receiving different executor instructions from the dispatcher, reducing the need for task-specific training and improving training efficiency across diverse task types.
3Reliability
If task-specific training is used, then the system performs well on trained tasks, but it cannot perform new tasks without specific training
Solution Approach 1:
The segmentation allows the executor to be trained on fundamental skills that are transferable across multiple tasks, while the dispatcher learns to adapt to new task descriptions. This separation enables the system to maintain reliable performance on core skills while gaining the ability to handle new tasks through the dispatcher's ability to generate appropriate executor instructions for unseen task types.
Solution Approach 2:
The executor is pre-trained on a set of fundamental skills and sub-tasks before being deployed. This preliminary training ensures reliable performance on these core skills, while the dispatcher can subsequently generate instructions for new tasks by combining and sequencing these pre-learned skills, enabling zero-shot learning capability without compromising reliability on trained tasks.
Data Source
AI summary
This specification describes systems and methods, implemented as computer programs on one or more computers in one or more locations, for controlling an agent to perform multiple different tasks in an environment. The described techniques partition the architecture of a controller into a dispatcher that understands the environment and an executor that understands how to control the agent, with a control channel between them that structures the partitioning. This allows implementations of the controller to generalize better.