Summary of Open-world Reinforcement Learning Over Long Short-term Imagination, by Jiajian Li et al.
Open-World Reinforcement Learning over Long Short-Term Imagination
by Jiajian Li, Qi Wang, Yunbo Wang, Xin Jin, Yang Li, Wenjun Zeng, Xiaokang Yang
First submitted to arxiv on: 4 Oct 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 presents a novel approach to training visual reinforcement learning agents in open-world scenarios, focusing on improving exploration efficiency across vast state spaces. The authors argue that current model-based methods are “short-sighted” and propose LS-Imagine, which extends the imagination horizon within a limited number of state transitions. This allows agents to explore behaviors leading to promising long-term feedback. The method builds upon a long short-term world model, simulating goal-conditioned jumpy state transitions and computing affordance maps by zooming in on specific areas within single images. This integration enables the incorporation of direct long-term values into behavior learning. Compared to state-of-the-art techniques, LS-Imagine demonstrates significant improvements in MineDojo. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how we can train machines to make good decisions in big, complex environments. Right now, most machines are not very good at this because they don’t think about the long-term consequences of their actions. The authors suggest a new way to do this by letting machines imagine different scenarios and explore what might happen if they take certain actions. This helps them learn which actions will lead to positive outcomes in the future. The method uses special computer simulations to help the machine understand how different objects interact with each other, making it better at making decisions. |
Keywords
* Artificial intelligence * Reinforcement learning
