Summary of Variational Delayed Policy Optimization, by Qingyuan Wu et al.
Variational Delayed Policy Optimization
by Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang, Yuhui Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Chao Huang
First submitted to arxiv on: 23 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 Variational Delayed Policy Optimization (VDPO), a novel framework for reinforcement learning (RL) in environments with delayed observation. VDPO reformulates delayed RL as a variational inference problem, which is divided into two steps: Temporal-Difference (TD) learning in the delay-free environment and behaviour cloning. The authors provide theoretical analysis of VDPO’s sample complexity and performance, and empirically demonstrate that it can achieve consistent performance with state-of-the-art (SOTA) methods while reducing the required samples by approximately 50% in the MuJoCo benchmark. This work tackles learning inefficiency issues in SOTA RL techniques, enabling more efficient exploration of delayed environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better when they can’t see everything right away. It proposes a new way to make this learning process faster and more efficient. The authors break down the problem into two steps: first, they learn how to act in a simpler environment without delays, and then they fine-tune their actions for the delayed environment. They show that this approach works just as well as current state-of-the-art methods but requires less data. This is important because it can help machines make better decisions when they’re not getting immediate feedback. |
Keywords
» Artificial intelligence » Inference » Optimization » Reinforcement learning
