Summary of Sapg: Split and Aggregate Policy Gradients, by Jayesh Singla et al.
SAPG: Split and Aggregate Policy Gradients
by Jayesh Singla, Ananye Agarwal, Deepak Pathak
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Systems and Control (eess.SY)
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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 Despite significant advancements in on-policy reinforcement learning (RL), recent studies have shown that current methods, such as Proximal Policy Optimization (PPO), fail to effectively leverage large-scale environments beyond a certain point. In fact, performance saturates due to sample inefficiency. To address this limitation, we propose SAPG, an innovative on-policy RL algorithm that splits and fuses parallelized environments using importance sampling. Our approach significantly outperforms vanilla PPO and other strong baselines in various challenging environments. By harnessing the power of parallelization, SAPG demonstrates its potential to revolutionize decision-making problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a powerful tool for making decisions. Recently, it’s become possible to collect lots of data for training these models using computers and simulations. But even with all that data, current methods can only go so far. They get stuck and don’t improve much after a certain point. To fix this, we created a new algorithm called SAPG. It takes big environments and breaks them into smaller chunks, then puts the pieces back together to make better decisions. Our SAPG algorithm does much better than other approaches in tough situations. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
