Summary of Demystifying the Recency Heuristic in Temporal-difference Learning, by Brett Daley et al.
Demystifying the Recency Heuristic in Temporal-Difference Learning
by Brett Daley, Marlos C. Machado, Martha White
First submitted to arxiv on: 18 Jun 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 analyzes the effectiveness of the “recency heuristic” in reinforcement learning, which assumes that stimuli closer to an acquired reward should be more heavily reinforced. It examines how this heuristic affects temporal credit assignment and proves that any return estimator satisfying it has certain desirable properties, such as convergence to the correct value function, fast contraction rate, and bounded worst-case variance. The paper also provides a counterexample showing that on-policy TD methods violating this heuristic can diverge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how our brains process rewards and punishments. It shows that when we learn from our experiences, it’s helpful to focus on what happened recently rather than long ago. This “recency heuristic” helps us make better decisions by giving more importance to recent events. The paper explains why this is a good way to learn and provides examples of when it doesn’t work. |
Keywords
» Artificial intelligence » Reinforcement learning
