Summary of Mad-td: Model-augmented Data Stabilizes High Update Ratio Rl, by Claas a Voelcker et al.
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL
by Claas A Voelcker, Marcel Hussing, Eric Eaton, Amir-massoud Farahmand, Igor Gilitschenski
First submitted to arxiv on: 11 Oct 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 The paper addresses the challenge of building deep reinforcement learning (RL) agents that find a good policy with few samples. Recent approaches have explored updating neural networks with large numbers of gradient steps, but this introduces instability to the training process. To mitigate this issue, the authors propose Model-Augmented Data for Temporal Difference learning (MAD-TD), which uses small amounts of generated data from a learned world model to stabilize high update-to-data ratios and achieve competitive performance on challenging tasks in the DeepMind control suite. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make deep reinforcement learning agents better at finding good policies with only a few samples. It’s hard to train these agents because they need lots of updates, but this makes them unstable. The authors fix this problem by using some fake data generated from a model of the world. This new method is called MAD-TD and it helps make RL training more stable. |
Keywords
* Artificial intelligence * Reinforcement learning
