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Action-driven contrastive representation for reinforcement learning.

Minbeom Kim1, Kyeongha Rho2, Yong-Duk Kim2

  • 1Graduate School of Artificial Intelligence, Seoul National University, Seoul, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces Action-Driven Auxiliary Task (ADAT), a novel method for reinforcement learning. ADAT improves sample-efficiency and generalization in control tasks by learning essential features from images.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Reinforcement learning (RL) from high-dimensional images faces challenges in sample-efficiency and generalization.
  • Existing representation learning methods struggle with environmental diversity and task-specific feature extraction.

Purpose of the Study:

  • To propose a novel contrastive representation method, Action-Driven Auxiliary Task (ADAT).
  • To enhance feature learning for improved sample-efficiency and generalization in RL control tasks.

Main Methods:

  • ADAT utilizes an augmented state-action dictionary to learn representations.
  • The method forces representations to focus on action-relevant features while ignoring irrelevant details.
  • Maximizes agreement between observations sharing the same actions.

Main Results:

  • ADAT significantly outperforms existing model-free and model-based RL algorithms.
  • Superior performance demonstrated on Atari and OpenAI ProcGen benchmarks.
  • The method shows marked improvements in sample-efficiency and generalization capabilities.

Conclusions:

  • ADAT offers a more robust and effective approach to representation learning in RL.
  • The proposed method addresses key limitations of prior works in diverse and complex environments.
  • ADAT advances the state-of-the-art in sample-efficient and generalizable reinforcement learning.