Summary of Kan V.s. Mlp For Offline Reinforcement Learning, by Haihong Guo et al.
KAN v.s. MLP for Offline Reinforcement Learning
by Haihong Guo, Fengxin Li, Jiao Li, Hongyan Liu
First submitted to arxiv on: 15 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 The paper introduces Kolmogorov-Arnold Networks (KAN), a neural network architecture that has garnered significant interest as a potential alternative to Multi-Layer Perceptions (MLP) in machine learning. KAN-based models have been shown to achieve comparable or better performance than MLP-based methods while requiring fewer parameters and being more explainable. The paper explores the application of KAN in offline reinforcement learning (RL) by incorporating it into actor-critic networks using conservative Q-learning (CQL). The authors evaluate the performance, parameter scales, and training efficiency of various KAN- and MLP-based CQL models on the D4RL benchmark for offline RL. The results demonstrate that KAN can achieve comparable performance to MLP with significantly fewer parameters, providing a valuable option for choosing base networks depending on the requirements of offline RL tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Kolmogorov-Arnold Networks are a new type of neural network that might be better than what we usually use. Scientists have been testing them and found they can do just as well or even better than what we’re used to, but with much less information needed. This paper looks at how KAN works in a specific kind of learning called offline reinforcement learning. The scientists tested different ways of using KAN and an old way we use called MLP. They did this on a special test called the D4RL benchmark. What they found is that KAN can do just as well as what we’re used to, but with much less information needed. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Reinforcement learning
