Summary of Actor-critic Reinforcement Learning with Phased Actor, by Ruofan Wu et al.
Actor-Critic Reinforcement Learning with Phased Actor
by Ruofan Wu, Junmin Zhong, Jennie Si
First submitted to arxiv on: 18 Apr 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 Policy gradient methods in actor-critic reinforcement learning have shown great promise for solving continuous optimal control problems. However, the trial-and-error nature of RL and the inherent randomness associated with solution approximations lead to variations in the learned optimal values and policies. This has hindered their deployment in real-life applications where control responses need to meet dynamic performance criteria deterministically. To address this issue, we propose a novel phased actor in actor-critic (PAAC) method that improves policy gradient estimation and the quality of the control policy. PAAC accounts for both Q-value and TD error in its actor update and proves qualitative properties such as learning convergence, solution optimality, and stability of system dynamics. Additionally, it shows variance reduction in policy gradient estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Policy gradient methods are used to solve complex problems in reinforcement learning. The problem is that the learned optimal values and policies can vary, making it hard to use them in real-life applications where control responses need to be precise. To fix this, we propose a new method called PAAC that improves policy gradient estimation. It’s like having a better map to find the best solution. We tested PAAC with some well-known methods and found that it works really well. |
Keywords
» Artificial intelligence » Reinforcement learning
