Summary of Satisficing Exploration For Deep Reinforcement Learning, by Dilip Arumugam et al.
Satisficing Exploration for Deep Reinforcement Learning
by Dilip Arumugam, Saurabh Kumar, Ramki Gummadi, Benjamin Van Roy
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 reinforcement-learning algorithm that enables agents to learn satisficing policies in complex environments. Unlike traditional approaches that strive for optimal solutions, this agent deliberately settles for satisfactory ones, obtained through lossy compression. The algorithm relies on model-based planning and is limited by its reliance on function approximation and high-dimensional observations. To address these limitations, the authors propose an extension that directly represents uncertainty over the optimal value function, allowing it to bypass model-based planning and learn satisficing policies more efficiently. The paper demonstrates the effectiveness of this algorithm through simple experiments using deep reinforcement-learning agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for machines to learn by settling for good enough solutions instead of perfect ones. It shows that even in very complex situations, an agent can still make progress and achieve its goals without having to explore every possibility. The authors use special tools from information theory to design this agent, which makes it more efficient at learning than traditional methods. They also show that their algorithm can learn the best solution when possible, but is faster and more effective at finding a good enough solution. |
Keywords
» Artificial intelligence » Reinforcement learning
