Summary of Amago-2: Breaking the Multi-task Barrier in Meta-reinforcement Learning with Transformers, by Jake Grigsby et al.
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
by Jake Grigsby, Justin Sasek, Samyak Parajuli, Daniel Adebi, Amy Zhang, Yuke Zhu
First submitted to arxiv on: 17 Nov 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 paper explores the idea of scaling up reinforcement learning (RL) policies to generalize across multiple tasks. By leveraging language models trained on diverse datasets, the authors demonstrate that meta-learning within sequence models can achieve similar effects as in-context learning. The challenge lies in adapting RL research to tackle multi-task optimization, which has received limited attention so far. To address this issue, the paper proposes a simple yet scalable solution by converting both agent’s actor and critic objectives into classification terms, decoupling optimization from return scales. This design enables significant progress in online multi-task adaptation and memory problems without explicit task labels, as demonstrated through large-scale comparisons on various benchmarks, including Meta-World ML45, Multi-Game Procgen, Multi-Task POPGym, Multi-Game Atari, and BabyAI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a machine that can learn from many different tasks without being explicitly taught. This paper explores ways to make this happen. Right now, machines are good at learning one task, but not as good at adapting to new ones. The authors suggest a way to make machines more flexible by changing how they think about rewards and goals. They test their idea on lots of games and simulations, showing that it can really help machines learn faster and better. |
Keywords
» Artificial intelligence » Attention » Classification » Meta learning » Multi task » Optimization » Reinforcement learning
