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Influence-aware memory architectures for deep reinforcement learning in POMDPs.

Miguel Suau1, Jinke He1, Elena Congeduti1

  • 1Intelligent Systems, Delft University of Technology, Delft, The Netherlands.

Neural Computing & Applications
|June 30, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces influence-aware memory for deep reinforcement learning agents. It improves training speed and performance by focusing recurrent layers on influential variables, overcoming limitations of standard recurrent neural networks (RNNs).

Keywords:
Conditional independenceInfluencePartial observabilityRecurrent neural networksReinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Agents often face perceptual limitations, leading to insufficient environmental information for optimal decision-making.
  • Recurrent Neural Networks (RNNs) are used in deep reinforcement learning to memorize past observations but face training and convergence challenges with high-dimensional data.
  • Partial observability in environments necessitates effective methods for agents to infer hidden state information from action-observation histories.

Purpose of the Study:

  • To propose a novel memory architecture, influence-aware memory, to address the training difficulties and performance limitations of standard RNNs in deep reinforcement learning.
  • To enhance the agent's ability to uncover hidden state information despite perceptual limitations.
  • To improve training speed, policy performance, and runtime efficiency compared to existing methods.

Main Methods:

  • Developed an influence-aware memory architecture that restricts recurrent layer inputs to variables influencing hidden state information.
  • Integrated a feedforward neural network to process non-influential observation variables.
  • Allowed information flow without mandatory storage in the RNN's internal memory, differing from standard RNN feedback mechanisms.

Main Results:

  • The influence-aware memory architecture significantly outperformed standard recurrent architectures in both training speed and policy performance.
  • The proposed method demonstrated reduced runtime compared to conventional approaches.
  • Achieved better performance scores than methods that stack multiple observations to mitigate partial observability.

Conclusions:

  • Influence-aware memory provides a theoretically inspired and effective solution for handling partial observability in deep reinforcement learning.
  • By enabling recurrent layers to focus on critical variables, the approach enhances learning efficiency and agent performance.
  • This architecture offers a promising direction for developing more capable and efficient intelligent agents in complex environments.