Summary of Meta-dt: Offline Meta-rl As Conditional Sequence Modeling with World Model Disentanglement, by Zhi Wang et al.
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement
by Zhi Wang, Li Zhang, Wenhao Wu, Yuanheng Zhu, Dongbin Zhao, Chunlin Chen
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed Meta Decision Transformer (Meta-DT) model aims to achieve highly capable generalists that can learn from diverse experiences and generalize to unseen tasks. By leveraging the sequential modeling ability of transformers and robust task representation learning via world model disentanglement, Meta-DT efficiently generalizes in offline meta-reinforcement learning. The framework pretrains a context-aware world model to learn a compact task representation, which is then injected as a contextual condition into a causal transformer to guide sequence generation. Additionally, history trajectories generated by the meta-policy serve as self-guided prompts to exploit architectural inductive bias. Notably, Meta-DT eliminates the requirement of expert demonstration or domain knowledge at test time. Experimental results on MuJoCo and Meta-World benchmarks demonstrate superior few and zero-shot generalization capacity compared to strong baselines while being more practical with fewer prerequisites. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Meta-DT is a new way for AI models to learn from different experiences and apply what they’ve learned to new situations without needing any special training or guidance. The model uses a combination of transformer-based learning and task representation to generalize well in various scenarios. It’s like having a super-smart assistant that can help you with lots of tasks without needing to be taught each one individually. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning » Representation learning » Transformer » Zero shot
