Summary of Neorl: Efficient Exploration For Nonepisodic Rl, by Bhavya Sukhija et al.
NeoRL: Efficient Exploration for Nonepisodic RL
by Bhavya Sukhija, Lenart Treven, Florian Dörfler, Stelian Coros, Andreas Krause
First submitted to arxiv on: 3 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 This paper introduces Nonepisodic Optimistic Reinforcement Learning (NeoRL), a novel approach for learning nonlinear dynamical systems from a single trajectory without resets. The proposed method, NeoRL, uses probabilistic models and optimistic planning to address uncertainty about unknown system dynamics. The authors provide a regret bound of O(ΓT√T) for general nonlinear systems with Gaussian process dynamics under continuity and bounded energy assumptions. Experimental results demonstrate that NeoRL achieves optimal average cost while minimizing regret in various deep reinforcement learning environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way to teach machines how to learn from limited information. They want to find the best solution for complex systems where we don’t know all the rules. The new approach is called Nonepisodic Optimistic Reinforcement Learning (NeoRL). It’s like being optimistic that you’ll make the right choice even when you’re not sure about the situation. The researchers tested their method on different scenarios and found it to be very effective in achieving good results while minimizing mistakes. |
Keywords
» Artificial intelligence » Reinforcement learning
