4 articles tagged with #decision-transformer. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce a Goal-Conditioned Decision Transformer designed for offline reinforcement learning in robotics, enabling multi-goal task learning from pre-collected datasets. The method demonstrates superior performance compared to online baselines on complex robotic tasks while maintaining effectiveness in sparse-reward environments with limited expert data.
Researchers propose OnDeFog, a reinforcement learning method that combines offline and online learning approaches to handle frame dropping in real-world applications. By integrating Decision Transformer mechanisms with online learning, OnDeFog demonstrates improved performance compared to existing offline methods when dealing with missing sensor data and communication delays.
Researchers introduce Return Augmented (REAG) method for Decision Transformer frameworks to improve offline reinforcement learning when training data comes from different dynamics than the target domain. The method aligns return distributions between source and target domains, with theoretical analysis showing it achieves optimal performance levels despite dynamics shifts.
The article appears to be about training a Decision Transformer, which is a machine learning model that treats reinforcement learning as a sequence modeling problem. However, the article body is empty, making it impossible to provide specific details about the implementation or methodology discussed.
