Summary of Is Exploration All You Need? Effective Exploration Characteristics For Transfer in Reinforcement Learning, by Jonathan C. Balloch et al.
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning
by Jonathan C. Balloch, Rishav Bhagat, Geigh Zollicoffer, Ruoran Jia, Julia Kim, Mark O. Riedl
First submitted to arxiv on: 2 Apr 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 paper explores the relationship between exploration methods in deep reinforcement learning (RL) and their impact on efficient online transfer learning in non-stationary Markov decision processes (MDPs). It investigates the effectiveness of eleven popular exploration algorithms in various transfer scenarios, identifying characteristics that positively affect performance and efficiency across different tasks. The study reveals correlations between certain algorithm features and improved results, as well as those that are specific to particular environment changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, researchers have been trying to find better ways to explore and learn when facing new situations in deep RL. They’ve discovered that some methods work better than others in different scenarios. This paper helps us understand which characteristics of exploration algorithms make them suitable for various transfer learning tasks. |
Keywords
» Artificial intelligence » Reinforcement learning » Transfer learning
