A scalable reinforcement learning framework inspired by hippocampal memory mechanisms for efficient contextual and...

Area of Science:

• Artificial Intelligence
• Neuroscience
• Cognitive Science

Background:

• Sequential decision-making in reinforcement learning (RL) presents significant challenges, particularly in context-dependent tasks.
• The brain's hippocampal system offers a model for memory integration crucial for efficient learning and adaptation.
• Existing RL methods often struggle with the complexity of sequential, context-rich environments.

Purpose of the Study:

• To introduce Hippocampal-Augmented Memory Integration (HAMI), a biologically inspired memory-based RL framework.
• To develop Hierarchical Contextual Sequences (HiCoS), a neuroscience-grounded environment for testing memory-based decision-making systems.
• To enhance learning efficiency, adaptability, and decision accuracy in context-dependent sequential tasks using HAMI.

Main Methods:

• Developed HAMI, incorporating symbolic indexing, hierarchical memory refinement, and structured episodic retrieval.
• Created HiCoS, a structured RL environment based on episodic memory and context-driven decision-making principles.
• Evaluated HAMI's performance against baseline methods in terms of accuracy, sample efficiency, and memory utilization.

Main Results:

• HAMI demonstrated high decision accuracy and improved sample efficiency.
• The framework achieved low memory utilization compared to traditional memory-based RL methods.
• HAMI exhibited significantly lower inference latency, suggesting potential for hardware acceleration.

Conclusions:

• HAMI provides a scalable and efficient memory-based RL framework for complex sequential tasks.
• The integration of biologically inspired memory mechanisms and symbolic representations is effective.
• HAMI's architecture is suitable for hardware acceleration, particularly with non-volatile memory (NVM)-based content-addressable memory (CAM).