Summary of An Advantage-based Optimization Method For Reinforcement Learning in Large Action Space, by Hai Lin et al.
An Advantage-based Optimization Method for Reinforcement Learning in Large Action Space
by Hai Lin, Cheng Huang, Zhihong Chen
First submitted to arxiv on: 17 Dec 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 an advantage-based optimization method to tackle the challenges of large, high-dimensional action spaces in reinforcement learning tasks. The traditional value-based approach struggles with convergence difficulties, instability, and computational complexity. To address this, the authors introduce an algorithm called Advantage Branching Dueling Q-network (ABQ), which incorporates a baseline mechanism to tune the action value of each dimension. This allows for the optimization of the learned policy for each dimension. Empirically, ABQ outperforms BDQ in various environments, achieving 3%, 171%, and 84% more cumulative rewards in HalfCheetah, Ant, and Humanoid, respectively. Additionally, ABQ demonstrates competitive performance compared to continuous action benchmark algorithms DDPG and TD3. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in computer learning called reinforcement learning. When machines learn from doing things, they need to decide what actions to take. But when there are many possible actions, it gets really hard for the machine to make good decisions. The authors created a new way of helping machines make better choices by looking at the advantages and disadvantages of each action. They call this method Advantage Branching Dueling Q-network (ABQ). It works better than some other methods in different situations, like helping robots move or humans control robots. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
