Summary of Diminishing Return Of Value Expansion Methods, by Daniel Palenicek et al.
Diminishing Return of Value Expansion Methods
by Daniel Palenicek, Michael Lutter, João Carvalho, Daniel Dennert, Faran Ahmad, Jan Peters
First submitted to arxiv on: 29 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper investigates the potential benefits of improved dynamics models in model-based value expansion methods for reinforcement learning. The authors find that longer rollout horizons can increase sample efficiency, but the gains quickly diminish with each additional step. Additionally, increasing model accuracy only marginally improves sample efficiency compared to learned models with identical horizons. These findings are significant because they suggest that the limitation of model-based value expansion methods cannot be attributed to model accuracy. Instead, other factors may be at play. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how good the models are in a special kind of machine learning called reinforcement learning. They test if making the models more accurate helps or not. What they found is that making the models better doesn’t really make a big difference. The important thing is to do many simulations and get more data, which makes the model learn faster. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
