Summary of Random Policy Enables In-context Reinforcement Learning Within Trust Horizons, by Weiqin Chen et al.
Random Policy Enables In-Context Reinforcement Learning within Trust Horizons
by Weiqin Chen, Santiago Paternain
First submitted to arxiv on: 25 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 This paper introduces State-Action Distillation (SAD), a novel approach to enable in-context reinforcement learning (ICRL) under random policies and contexts. SAD addresses the challenge of acquiring optimal or well-trained behavior policies for real-world environments, which is typically required by state-of-the-art ICRL algorithms like Algorithm Distillation, Decision Pretrained Transformer, and Decision Importance Transformer. Instead, SAD generates an effective pretraining dataset guided solely by random policies, allowing for zero-shot generalization to new tasks not encountered during pretraining. This approach outperforms the best baseline in both offline (236.3%) and online (135.2%) evaluations across multiple popular ICRL benchmark environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better without needing to train from scratch. Imagine you’re trying to solve a puzzle, but you need help getting started. That’s kind of like what happens when we try to teach machines new things. The machine has to learn from examples it hasn’t seen before, which can be hard. This paper shows how to make that process easier by using random clues to help the machine get started. The result is a big improvement in how well the machine can solve problems on its own. |
Keywords
» Artificial intelligence » Distillation » Generalization » Pretraining » Reinforcement learning » Transformer » Zero shot
