Summary of Vdsc: Enhancing Exploration Timing with Value Discrepancy and State Counts, by Marius Captari et al.
VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts
by Marius Captari, Remo Sasso, Matthia Sabatelli
First submitted to arxiv on: 26 Mar 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 In this paper, researchers explore how to determine when an agent should start exploring in deep reinforcement learning. They examine existing strategies, including the simple yet effective epsilon-greedy approach, which is easy to implement and works well across many domains. However, this method simply switches between exploration and exploitation without considering the agent’s internal state. The authors propose a new approach called Value Discrepancy and State Counts through Homeostasis (VDSC) that leverages the agent’s internal state to decide when to explore. They demonstrate the effectiveness of VDSC on the Atari suite, showing it outperforms traditional methods like epsilon-greedy and Boltzmann, as well as more sophisticated techniques like Noisy Nets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists investigate how machines decide whether to try new things or stick with what they know. They look at simple ways that computers make decisions, like the “epsilon-greedy” method, which is good for many situations but doesn’t consider the computer’s internal state. The researchers then suggest a new way to make decisions called VDSC (Value Discrepancy and State Counts through Homeostasis), which takes into account the computer’s internal state. By using this new approach, computers can learn more efficiently than before. |
Keywords
* Artificial intelligence * Reinforcement learning
