Summary of Multi-state Td Target For Model-free Reinforcement Learning, by Wuhao Wang et al.
Multi-State TD Target for Model-Free Reinforcement Learning
by Wuhao Wang, Zhiyong Chen, Lepeng Zhang
First submitted to arxiv on: 26 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 In this paper, researchers enhance temporal difference (TD) learning in reinforcement learning by introducing an enhanced multi-state TD (MSTD) target. The traditional TD target only considers a single subsequent state, whereas the MSTD target incorporates the estimated values of multiple subsequent states. This novel approach leads to significant improvements in learning performance when combined with actor-critic algorithms, including DDPG and SAC. Experimental results demonstrate the effectiveness of these enhanced algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves temporal difference (TD) learning in reinforcement learning by using a new target that considers many future states instead of just one. This helps agents learn faster and make better decisions. The researchers test their new approach with different types of agents and show that it works well. |
Keywords
» Artificial intelligence » Reinforcement learning
