Summary of Discretizing Continuous Action Space with Unimodal Probability Distributions For On-policy Reinforcement Learning, by Yuanyang Zhu et al.
Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning
by Yuanyang Zhu, Zhi Wang, Yuanheng Zhu, Chunlin Chen, Dongbin Zhao
First submitted to arxiv on: 1 Aug 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 The paper proposes a novel architecture for on-policy reinforcement learning in continuous control tasks by discretizing the action space. The approach addresses the issue of exploding action spaces by constraining the discrete policy to be unimodal using Poisson probability distributions, allowing for better leverage of continuity in the underlying action space. Experimental results demonstrate faster convergence and higher performance for challenging control tasks, including Humanoid. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to help computers learn from rewards in complex situations. It uses discrete actions instead of continuous ones, which makes it easier to optimize. However, this can cause problems if there are many possible actions. The solution is to make the policy unimodal, meaning it only chooses one action at a time. This helps the computer learn faster and do better in tasks like controlling robots. |
Keywords
* Artificial intelligence * Probability * Reinforcement learning
