Relativized hierarchical decomposition of Markov decision processes

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Reinforcement Learning (RL) enables sequential decision-making under uncertainty with limited environmental knowledge and feedback.
• Effective operation in complex environments necessitates learning agents forming useful abstractions by ignoring irrelevant details.
• Deriving a single, universally useful representation for large-scale problems remains challenging.

Purpose of the Study:

• To introduce a hierarchical Reinforcement Learning (RL) framework.
• To incorporate an algebraic framework for modeling task-specific abstraction within RL.
• To explore the concept of homomorphism of a Markov Decision Process (MDP) for abstraction.

Main Methods:

• Describing a hierarchical RL framework.
• Utilizing an algebraic framework for task-specific abstraction.
• Extending the basic MDP homomorphism framework to include selective attention.

Main Results:

• The proposed framework facilitates the creation of task-specific abstractions in RL.
• The MDP homomorphism provides a foundation for understanding and implementing abstraction.
• Extensions to the framework accommodate notions of selective attention.

Conclusions:

• Hierarchical RL combined with algebraic abstraction offers a powerful approach for complex decision-making.
• The MDP homomorphism framework is a key component for developing effective abstractions in RL.
• This work advances the understanding of abstraction in RL for improved agent performance.