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Information Optimization and Transferable State Abstractions in Deep Reinforcement Learning.

Diego Gomez, Nicanor Quijano, Luis Felipe Giraldo

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 22, 2022

Summary

This summary is machine-generated.

This study introduces self-supervised learning of discrete representations for artificial agents. This approach enables reusable knowledge across tasks, improving sample efficiency and generalization abilities.

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Area of Science:

Artificial Intelligence
Machine Learning
Deep Reinforcement Learning

Background:

Standard deep reinforcement learning models are task-specific, limiting knowledge reusability.
Humans and animals exhibit lifelong learning and knowledge transfer, unlike current AI methods.

Purpose of the Study:

To develop a method for leveraging prior knowledge to solve future tasks in artificial agents.
To enable artificial agents to generalize abilities to new, unseen tasks.

Main Methods:

Learning discrete representations of sensory inputs through self-supervision.
Employing an information-theoretic approach to learn high-level abstractions.
Applying the method to locomotive and optimal control tasks.

Main Results:

Learned discrete representations provide a common abstraction across multiple tasks.
The method facilitates information transference, enhancing sample efficiency.
Improved performance observed in both known and previously unknown tasks.

Conclusions:

Self-supervised learning of discrete representations is a viable path towards generalizable artificial agents.
This approach significantly boosts sample efficiency in reinforcement learning.
Enables artificial agents to acquire and reuse knowledge across diverse tasks.