Summary of Retrieval-augmented Decision Transformer: External Memory For In-context Rl, by Thomas Schmied et al.
Retrieval-Augmented Decision Transformer: External Memory for In-context RL
by Thomas Schmied, Fabian Paischer, Vihang Patil, Markus Hofmarcher, Razvan Pascanu, Sepp Hochreiter
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Retrieval-Augmented Decision Transformer (RA-DT) is a novel approach that enables Reinforcement Learning (RL) agents to learn new tasks by observing a few exemplars in their context, without requiring entire episodes. Unlike prior methods, RA-DT employs an external memory mechanism that stores past experiences and retrieves relevant sub-trajectories for the current situation, without requiring additional training or domain-specific knowledge. The proposed model outperforms baselines on grid-world environments, using only a fraction of their context length, while also demonstrating capabilities in robotics simulations and procedurally-generated video games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In-context learning is a technique that allows models to learn new tasks by observing a few examples. In Reinforcement Learning (RL), this ability has been observed recently, but previous methods require entire episodes. To address this limitation, a new approach called Retrieval-Augmented Decision Transformer (RA-DT) was developed. RA-DT uses an external memory mechanism to store past experiences and retrieve relevant parts for the current situation. This method outperforms others in grid-world environments and can also be used in robotics simulations and video games. |
Keywords
» Artificial intelligence » Context length » Reinforcement learning » Transformer
