Summary of Conformal Symplectic Optimization For Stable Reinforcement Learning, by Yao Lyu et al.
Conformal Symplectic Optimization for Stable Reinforcement Learning
by Yao Lyu, Xiangteng Zhang, Shengbo Eben Li, Jingliang Duan, Letian Tao, Qing Xu, Lei He, Keqiang Li
First submitted to arxiv on: 3 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 The proposed relativistic adaptive gradient descent (RAD) algorithm enhances long-term training stability for deep reinforcement learning (RL) agents by conceptualizing neural network (NN) training as the evolution of a conformal Hamiltonian system. RAD incorporates principles from special relativity to limit parameter updates below a finite speed, effectively mitigating abnormal gradient influences. The algorithm’s sublinear convergence is proven under general nonconvex settings, and experimental results show RAD outperforming nine baseline optimizers with five RL algorithms across twelve environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to train deep reinforcement learning agents. They came up with an algorithm called RAD that helps the agent learn faster and better. It’s like having a special speed limit for how much the agent can change its behavior at one time. This makes it more stable and able to learn from its mistakes. The authors tested their algorithm on lots of different games and situations, and it did really well compared to other ways of training agents. |
Keywords
» Artificial intelligence » Gradient descent » Neural network » Reinforcement learning
