Summary of Dodt: Enhanced Online Decision Transformer Learning Through Dreamer’s Actor-critic Trajectory Forecasting, by Eric Hanchen Jiang et al.
DODT: Enhanced Online Decision Transformer Learning through Dreamer’s Actor-Critic Trajectory Forecasting
by Eric Hanchen Jiang, Zhi Zhang, Dinghuai Zhang, Andrew Lizarraga, Chenheng Xu, Yasi Zhang, Siyan Zhao, Zhengjie Xu, Peiyu Yu, Yuer Tang, Deqian Kong, Ying Nian Wu
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to combining world models with decision transformers in reinforcement learning, aiming to improve efficiency and effectiveness. The proposed methodology, which combines the Dreamer algorithm’s anticipatory trajectories with the adaptive learning strengths of the Online Decision Transformer, enables parallel training that enhances contextual decision-making. Empirical results on challenging benchmarks demonstrate notable improvements in sample efficiency and reward maximization over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This breakthrough paper helps machines learn complex tasks by combining two powerful algorithms: Dreamer and Online Decision Transformer. By generating anticipatory trajectories, the combined approach accelerates learning and makes better decisions. The result is a more efficient and robust model-based reinforcement learning system that can handle diverse and dynamic scenarios. |
Keywords
» Artificial intelligence » Reinforcement learning » Transformer
