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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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Self-Referencing Agents for Unsupervised Reinforcement Learning.

Andrew Zhao1, Erle Zhu2, Rui Lu1

  • 1Department of Automation, BNRist, Tsinghua University, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Self-Reference (SR), an add-on module for unsupervised reinforcement learning. SR enhances performance and sample efficiency by stabilizing rewards and preserving exploratory behaviors.

Keywords:
FinetuningPretrainingReinforcement learningUnsupervised reinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Reinforcement Learning

Background:

  • Unsupervised reinforcement learning (URL) methods often struggle with reward nonstationarity during pre-training.
  • Exploratory behavior can be forgotten during the fine-tuning phase of URL.

Purpose of the Study:

  • Introduce Self-Reference (SR), a novel add-on module for URL.
  • Address reward nonstationarity and the forgetting of exploratory behaviors in URL.

Main Methods:

  • SR stabilizes intrinsic rewards using historical referencing during pre-training.
  • SR preserves valuable exploratory behaviors during fine-tuning.

Main Results:

  • Significantly boosts performance and sample efficiency of existing URL model-free methods.
  • Improves the Intrinsic Quality Measure (IQM) by up to 17%.
  • Reduces the Optimality Gap by 31% on the Unsupervised Reinforcement Learning Benchmark.

Conclusions:

  • SR is a generally applicable and compatible add-on module for existing URL methods.
  • SR effectively mitigates reward nonstationarity and retains exploratory skills.