Successor Feature Neural Network for Agent Task Adaptation

Solution Overview

Problem

Collecting training data for new tasks in machine learning, especially for reinforcement learning, is expensive in terms of time and computational resources, and can cause damage to agents and environments.

Innovation Solution

The system employs successor features to transfer knowledge from already learned tasks, minimizing the need for additional environment interaction and reducing computational resources required for learning new tasks, using a successor feature neural network shared across training tasks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If reinforcement learning is used to learn new tasks by collecting training data from environment interaction, then the agent can learn policies for new tasks, but the time and computational resources required increase significantly

Engineering Contradiction:

Improveagent's ability to learn new tasksVSAvoidtime required for data collection and training

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary action by pre-computing and storing successor features during a training phase before the agent needs to perform new tasks. These pre-computed successor features are stored in a database and can be quickly queried during inference, eliminating the need to re-collect and re-process training data when new tasks arise. This preliminary computation of task representations enables rapid adaptation to new tasks without time-consuming retraining.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a copy of the training data structure by storing successor features in a database that can be queried for new tasks. Instead of re-collecting raw training data for each new task, the system copies relevant pre-computed successor features from the database to generate policies for new tasks. This copying approach significantly reduces the time and computational resources needed for learning new tasks while maintaining the agent's adaptability.

Inventive Principle:

Principle #26Copying

2Adaptability or versatility

If reinforcement learning is used to learn new tasks through environment interaction, then the agent can acquire new policies, but computational resources and energy consumption increase

Engineering Contradiction:

Improveagent's ability to learn new tasksVSAvoidcomputational resources and energy for training

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary action by pre-computing and storing success features during a training phase before the agent needs to perform new tasks. These pre-computed successor features are stored in a database and can be quickly queried during inference, eliminating the need to re-collect and re-process training data when new tasks arise. This preliminary computation of task representations enables rapid adaptation to new tasks without time-consuming retraining.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a copy of the training data structure by storing successor features in a database that can be queried for new tasks. Instead of re-collecting raw training data for each new task, the system copies relevant pre-computed successor features from the database to generate policies for new tasks. This copying approach significantly reduces the time and computational resources needed for learning new tasks while maintaining the agent's adaptability.

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If the agent interacts with the environment to collect training data for new tasks, then learning can proceed, but wear and tear on the agent and environment increases

Engineering Contradiction:

Improveagent's ability to learn new tasksVSAvoidwear and tear on agent and environment

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary action by pre-computing and storing success features during a training phase before the agent needs to perform new tasks. These pre-computed successor features are stored in a database and can be quickly queried during inference, eliminating the need to re-collect and re-process training data when new tasks arise. This preliminary computation of task representations enables rapid adaptation to new tasks without time-consuming retraining.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a copy of the training data structure by storing successor features in a database that can be queried for new tasks. Instead of re-collecting raw training data for each new task, the system copies relevant pre-computed success features from the database to generate policies for new tasks. This copying approach significantly reduces the time and computational resources needed for learning new tasks while maintaining the agent's adaptability.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20240386281A1Controlling agents by transferring successor features to new tasks

Publication Date: 2024.11.21 GDM HOLDING LLC

AI summary

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium. for controlling agents by transferring successor features to new tasks.