Summary of Highway Reinforcement Learning, by Yuhui Wang et al.
Highway Reinforcement Learning
by Yuhui Wang, Miroslav Strupl, Francesco Faccio, Qingyuan Wu, Haozhe Liu, Michał Grudzień, Xiaoyang Tan, Jürgen Schmidhuber
First submitted to arxiv on: 28 May 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 The proposed method tackles a fundamental challenge in reinforcement learning (RL), namely learning from multi-step off-policy data. The approach addresses limitations in importance sampling (IS) and IS-free methods like n-step Q-learning, which can underestimate the optimal value function (VF). To overcome this issue, the authors introduce a novel IS-free, multi-step off-policy method featuring a highway gate that controls information flow from distant future time steps. This allows for safe learning with arbitrary lookahead depths and behavioral policies, outperforming existing algorithms on tasks with delayed rewards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper discusses how to learn from multiple actions taken by different policies in reinforcement learning (RL). The current methods either use importance sampling (IS) or look ahead a certain number of steps. However, these methods have limitations. IS can be noisy and free methods may underestimate the best action. To fix this issue, researchers introduced a new method that has a special gate to control information from far away. This allows for learning even when looking very far into the future. The new method performs well in games where rewards are given only at the end. |
Keywords
* Artificial intelligence * Reinforcement learning
