Value Explicit Pretraining for Learning Transferable Representations.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Vision

Background:

• Reinforcement learning agents struggle with understanding visual inputs amid environmental changes.
• Generalizable representations are crucial for efficient transfer learning in dynamic environments.

Purpose of the Study:

• To introduce Value Explicit Pretraining (VEP), a novel method for learning generalizable representations in transfer reinforcement learning.
• To enable efficient adaptation to new tasks by learning representations invariant to environmental dynamics and appearance variations.

Main Methods:

• VEP utilizes suboptimal, unlabeled demonstration data (observations and sparse rewards) for pretraining an encoder.
• A self-supervised contrastive loss is employed, relating states across tasks using Monte Carlo value estimates to reflect task progress.
• This results in temporally smooth representations that capture task objectives.

Main Results:

• VEP outperforms state-of-the-art pretraining methods in generalizing to unseen tasks.
• Experiments on Ant locomotion, a navigation simulator, and Atari benchmarks demonstrate significant improvements.
• VEP achieved up to a 2x increase in rewards and up to a 3x improvement in sample efficiency.

Conclusions:

• VEP effectively learns generalizable representations from suboptimal data for transfer reinforcement learning.
• The method shows superior performance in adapting to new tasks compared to existing approaches.
• VEP offers a promising direction for enhancing the efficiency and adaptability of reinforcement learning agents.