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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Efficient Reinforcement Learning from Demonstration via Bayesian Network-Based Knowledge Extraction.

Yichuan Zhang1, Yixing Lan1, Qiang Fang1

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.

Computational Intelligence and Neuroscience
|October 4, 2021
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Summary
This summary is machine-generated.

Reinforcement learning from demonstration (RLfD) using Bayesian networks extracts abstract knowledge, improving policy generalization and robustness. This novel RLBNK method enhances learning efficiency in reinforcement learning (RL).

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Reinforcement learning from demonstration (RLfD) leverages expert actions to guide policy learning.
  • Existing RLfD methods often treat demonstrations as low-level data, limiting generalization and robustness.
  • Human knowledge is interpretable and generalizable, offering potential for improved RLfD.

Purpose of the Study:

  • To develop a novel RLfD method, Reinforcement Learning from demonstration via Bayesian Network-based Knowledge (RLBNK), for enhanced knowledge extraction and policy optimization.
  • To improve the generalization capability and robustness of reinforcement learning policies by integrating abstract knowledge from demonstrations.
  • To enhance learning efficiency in reinforcement learning (RL) algorithms.

Main Methods:

  • Extracting abstract concepts from expert demonstrations using the node influence with Wasserstein distance metric (NIW) algorithm.
  • Employing a Bayesian network for knowledge learning and inference on abstract data, generating a coarse policy with confidence levels.
  • Utilizing a reinforcement learning-based refine module to optimize the coarse policy when confidence is low, creating a hybrid policy.

Main Results:

  • The RLBNK method significantly improves learning efficiency compared to baseline RL algorithms in both normal and sparse reward settings.
  • RLBNK demonstrates superior generalization capability over traditional RLfD methods.
  • The proposed method exhibits enhanced robustness compared to baseline approaches.

Conclusions:

  • RLBNK effectively extracts and utilizes abstract knowledge from demonstrations via Bayesian networks, outperforming existing RLfD techniques.
  • The hybrid policy optimization approach in RLBNK leads to improved performance, generalization, and robustness in reinforcement learning tasks.
  • This work highlights the potential of integrating interpretable human knowledge into RL for more efficient and reliable agents.