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Memory Transformation Enhances Reinforcement Learning in Dynamic Environments.

Adam Santoro1,2, Paul W Frankland3,2,4,5, Blake A Richards6,7

  • 1Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1AB, Canada.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|December 2, 2016
PubMed
Summary
This summary is machine-generated.

Memory transformation, the shift from detailed to gist-like memories, enhances reinforcement learning. This process optimizes an agent's behavior across different timescales in dynamic environments.

Keywords:
computational modelingdecision makingepisodic memorymemory transformationreinforcement learningschema

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Memory consolidation involves a transition from detailed episodic memories to generalized schematic memories, a process termed memory transformation.
  • Existing theories focus on memory transformation's role in reducing interference and building environmental models.
  • The impact of memory transformation on an agent's real-time reinforcement learning in dynamic settings remains underexplored.

Purpose of the Study:

  • To investigate the benefit of memory transformation in enhancing reinforcement learning.
  • To demonstrate how memory transformation optimizes behavior across multiple timescales.
  • To reframe the understanding of memory transformation beyond interference avoidance.

Main Methods:

  • Developed a neural network model for a foraging task with continuously changing reward locations.
  • Trained the network using both episodic (specific locations) and schematic (statistical patterns) memories.
  • Analyzed the network's performance and strategy shifts over time.

Main Results:

  • Switching from episodic to schematic memory strategies over time improved performance in the dynamic foraging task.
  • This enhancement is attributed to the correlation of reward locations in the short-term and their regression to a stable distribution in the long-term.
  • Optimal memory utilization is dependent on the statistics of the environment.

Conclusions:

  • Memory transformation offers a significant advantage for reinforcement learning in dynamic environments.
  • The process optimizes an agent's adaptive behavior across various timescales.
  • This study shifts the focus of memory transformation research towards its role in behavioral optimization.