Summary of Revisiting Experience Replayable Conditions, by Taisuke Kobayashi
Revisiting Experience Replayable Conditions
by Taisuke Kobayashi
First submitted to arxiv on: 15 Feb 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 In this paper, researchers explore the application of experience replay (ER) in reinforcement learning, challenging the common assumption that ER is only suitable for off-policy algorithms. They propose modified existing algorithms that satisfy stricter “experience replayable conditions” (ERC), highlighting the role of policy improvement instability as a key factor. The authors derive stabilization tricks to mitigate repulsive forces and replays of inappropriate experiences, demonstrating their effectiveness through numerical simulations using an advantage actor-critic algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Experience replay in reinforcement learning is usually used with off-policy algorithms, but some researchers have tried it with on-policy algorithms too. This paper looks at why this might be possible. The authors think that the main problem is that the policy improvements are unstable, which makes it hard to use ER. They suggest ways to fix this and show that their ideas work by testing them with a special kind of algorithm called an advantage actor-critic. |
Keywords
* Artificial intelligence * Reinforcement learning
