Summary of I Know How: Combining Prior Policies to Solve New Tasks, by Malio Li et al.
I Know How: Combining Prior Policies to Solve New Tasks
by Malio Li, Elia Piccoli, Vincenzo Lomonaco, Davide Bacciu
First submitted to arxiv on: 14 Jun 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 Multi-Task Reinforcement Learning (MTRL) aims to develop agents that adapt to new scenarios without forgetting previously learned tasks. However, catastrophic forgetting and high computational demands pose significant challenges. To overcome these limitations, MTRL agents should leverage prior knowledge when facing new problems. While various methodologies have been proposed, they lack a common framework. In this work, we introduce I Know How (IKH), a novel approach that formalizes modularity and compositionality of knowledge to enable efficient learning and adaptation in dynamic environments. We demonstrate IKH’s effectiveness in a simulated driving scenario, comparing its performance with state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a robot or computer program that can learn from experience and adapt to new situations without forgetting what it already knows. This is called Multi-Task Reinforcement Learning (MTRL). The problem is that this kind of learning requires a lot of computational power and it’s hard for the program to remember everything it has learned in the past. To solve this challenge, researchers have proposed different methods, but they don’t all work together seamlessly. In this study, scientists introduce a new approach called I Know How (IKH) that helps programs learn and adapt more efficiently by organizing their knowledge in a special way. They tested IKH on a simulated driving task and found it outperformed other approaches. |
Keywords
* Artificial intelligence * Multi task * Reinforcement learning
