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Learning generalizable agents via self-supervised exploration.

Baoxian Liang1, Lihong Xu1, Zhichao Deng1

  • 1College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.

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Summary
This summary is machine-generated.

This study introduces a new self-supervised exploration framework to improve visual reinforcement learning generalization. The method enhances sample efficiency and agent adaptability in unseen environments.

Keywords:
Self-supervised learningTask-relevant representationsVisual reinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Generalization is a major hurdle in visual reinforcement learning (RL), where agents trained on limited views often fail in new environments.
  • Directly applying self-supervised learning (SSL) to visual RL can decrease sample efficiency and training stability, hindering generalization.
  • Existing methods struggle to effectively integrate representation learning with RL decision-making for improved generalization.

Purpose of the Study:

  • To propose a novel self-supervised exploration framework for learning dynamics-relevant representations in visual reinforcement learning.
  • To enhance the integration of representation learning into the RL decision-making process for better generalization.
  • To improve sample efficiency and adaptability of RL agents in unseen environments.

Main Methods:

  • The proposed framework comprises two modules: a visual discrepancy inference module (VDIM) and an exploration via distributional discrepancy module (EDDM).
  • VDIM learns shared features across different views to retain task-relevant information and filter out irrelevant data.
  • EDDM actively explores the environment to identify changed features, improving agent awareness of critical visual information for decision-making.

Main Results:

  • The framework significantly outperforms prior methods in generalization capabilities.
  • Demonstrated salient improvements in sample efficiency compared to existing approaches.
  • The method enables agents to adapt more quickly to new scenarios by enhancing self-awareness of visual features.

Conclusions:

  • The novel self-supervised exploration framework effectively addresses the generalization challenge in visual reinforcement learning.
  • Integrating dynamics-relevant representations through VDIM and EDDM leads to superior performance and adaptability.
  • This approach offers a promising direction for developing more robust and efficient visual RL agents.