Summary of Off-oab: Off-policy Policy Gradient Method with Optimal Action-dependent Baseline, by Wenjia Meng et al.
Off-OAB: Off-Policy Policy Gradient Method with Optimal Action-Dependent Baseline
by Wenjia Meng, Qian Zheng, Long Yang, Yilong Yin, Gang Pan
First submitted to arxiv on: 4 May 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 This paper proposes an off-policy policy gradient method to solve challenging reinforcement learning problems with high sample efficiency. By introducing an optimal action-dependent baseline (Off-OAB), the method mitigates the variance issue in off-policy policy gradients, achieving better performance than state-of-the-art methods on six representative tasks from OpenAI Gym and MuJoCo. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us solve difficult learning problems by using a new way to improve our policies. It uses special data that we didn’t collect while making decisions, which is helpful but tricky. The authors found a way to reduce the noise in this process, making it work better. They tested their method on six challenging tasks and did better than previous methods. |
Keywords
» Artificial intelligence » Reinforcement learning
