Summary of Adacred: Adaptive Causal Decision Transformers with Feature Crediting, by Hemant Kumawat and Saibal Mukhopadhyay
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting
by Hemant Kumawat, Saibal Mukhopadhyay
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
GrooveSquid.com Paper Summaries
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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 reinforcement learning (RL) by formulating it as a sequence modeling problem, where models predict future actions based on historical state-action-reward sequences. The current approaches require long trajectory sequences to model the environment in offline RL settings, but these models tend to over-rely on memorizing long-term representations, which impairs their ability to effectively attribute importance to trajectories and learned representations based on task-specific relevance. The authors introduce AdaCred, a novel approach that represents trajectories as causal graphs built from short-term action-reward-state sequences. The model adaptively learns control policy by crediting and pruning low-importance representations, retaining only those most relevant for the downstream task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to learn new skills without actually doing them. It’s like teaching a robot how to do something, but instead of showing it, you just give it information about what happened before. This makes it easier for the robot to understand what’s important and what’s not. The authors came up with a new way to do this that works better than old ways. |
Keywords
» Artificial intelligence » Pruning » Reinforcement learning
