Summary of Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers, by Gautham Vasan et al.
Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers
by Gautham Vasan, Mohamed Elsayed, Alireza Azimi, Jiamin He, Fahim Shariar, Colin Bellinger, Martha White, A. Rupam Mahmood
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO); Systems and Control (eess.SY)
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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 presents a novel approach to deep policy gradient methods, addressing limitations of existing methods that require large replay buffers or expensive batch updates. The authors demonstrate that these methods fail when applied to real systems with resource-limited computers. They propose the Action Value Gradient (AVG) method and normalization techniques to address instability in incremental learning. AVG outperforms other incremental methods on robotic simulation benchmarks, achieving comparable performance to batch policy gradient methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making deep learning work better for robots. Right now, most deep learning methods need a lot of memory or lots of calculations to learn from experience. But real robots don’t have that much power! The authors show that these methods actually fail when they try to use them with robots. They create a new way to do this called Action Value Gradient (AVG) and add some extra tricks to make it work better. With AVG, the robot can learn from what it does without needing lots of memory or calculations. |
Keywords
* Artificial intelligence * Deep learning
