Summary of Kolmogorov-arnold Network For Online Reinforcement Learning, by Victor Augusto Kich et al.
Kolmogorov-Arnold Network for Online Reinforcement Learning
by Victor Augusto Kich, Jair Augusto Bottega, Raul Steinmetz, Ricardo Bedin Grando, Ayano Yorozu, Akihisa Ohya
First submitted to arxiv on: 9 Aug 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 Kolmogorov-Arnold Networks (KANs) have emerged as an alternative to Multi-Layer Perceptrons (MLPs), offering universal function approximation capabilities with reduced parameters and memory requirements. This study investigates the application of KANs as function approximators within Proximal Policy Optimization (PPO). The authors compare this approach’s performance to the original MLP-based PPO using the DeepMind Control Proprio Robotics benchmark. The results show that the KAN-based reinforcement learning algorithm can achieve comparable performance to its MLP-based counterpart, often with fewer parameters. This suggests that KANs may offer a more efficient option for reinforcement learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Kolmogorov-Arnold Networks are a new type of neural network that might be better than the usual ones. Researchers wanted to see if these networks could help improve how well a computer learns by using rewards and punishments, like a game. They tested this idea on a special kind of robot and found out that it worked almost as well as the old way, but with fewer parts and less memory needed. This is exciting because it means we might be able to make computers learn faster or more efficiently. |
Keywords
» Artificial intelligence » Neural network » Optimization » Reinforcement learning
