Summary of Three Dogmas Of Reinforcement Learning, by David Abel et al.
Three Dogmas of Reinforcement Learning
by David Abel, Mark K. Ho, Anna Harutyunyan
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 research paper challenges three fundamental assumptions in modern reinforcement learning, which have shaped the field’s development. The first assumption, known as the “environment spotlight,” prioritizes modeling environments over agents. The second assumption views learning as finding solutions to tasks rather than adaptation. The third and most influential assumption is the “reward hypothesis,” suggesting that all goals can be represented as maximizing a reward signal. To unlock the full potential of reinforcement learning in understanding intelligent agents, the authors propose shedding these dogmas and adopting a more nuanced approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning has been stuck on three old ideas for too long! These “dogmas” make us focus on environments instead of agents, think of learning as solving tasks rather than adapting, and assume that all goals are about getting rewards. The authors want to break free from these outdated ideas and start fresh. |
Keywords
* Artificial intelligence * Reinforcement learning
