Summary of Double Actor-critic with Td Error-driven Regularization in Reinforcement Learning, by Haohui Chen et al.
Double Actor-Critic with TD Error-Driven Regularization in Reinforcement Learning
by Haohui Chen, Zhiyong Chen, Aoxiang Liu, Wentuo Fang
First submitted to arxiv on: 28 Sep 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 In this paper, researchers propose a novel algorithm called TDDR (Temporal Difference Error-driven Regularization) for better value estimation in reinforcement learning. The algorithm is based on the double actor-critic framework and employs two actors paired with critics to fully leverage their advantages. Additionally, it introduces an innovative critic regularization architecture. Compared to classical deterministic policy gradient-based algorithms, TDDR provides superior estimation without introducing additional hyperparameters, making it easier to design and implement. Experimental results show that TDDR performs competitively in challenging continuous control tasks compared to benchmark algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper suggests a new way to estimate values in reinforcement learning. It’s called TDDR, which stands for Temporal Difference Error-driven Regularization. The algorithm uses two actors and critics to help make better choices. This is different from other methods that don’t use both actors and critics together. What’s cool about TDDR is that it doesn’t need extra settings to work well. Scientists tested this method on hard tasks, like controlling robots, and found that it does pretty well compared to other ways of doing things. |
Keywords
» Artificial intelligence » Regularization » Reinforcement learning
