Summary of Proximal Policy Distillation, by Giacomo Spigler
Proximal Policy Distillation
by Giacomo Spigler
First submitted to arxiv on: 21 Jul 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 Medium Difficulty Summary: We introduce Proximal Policy Distillation (PPD), a novel method that combines student-driven distillation and Proximal Policy Optimization (PPO) to improve sample efficiency and leverage additional rewards during distillation. PPD is compared with two alternatives, student-distill and teacher-distill, across various reinforcement learning environments, including ATARI, Mujoco, and Procgen. The results show that PPD achieves better sample efficiency and produces superior student policies compared to typical policy distillation approaches. Moreover, PPD demonstrates greater robustness when distilling policies from imperfect demonstrations. The code is released as part of the stable-baselines3 library for facilitating policy distillation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Scientists have developed a new way to make computers learn faster and better. They call it Proximal Policy Distillation (PPD). PPD helps computers learn from imperfect teachers by using two techniques: student-driven learning and Proximal Policy Optimization. The researchers tested PPD on various computer games and simulations, and found that it makes the computer learn faster and more accurately than usual. This new method can help create better artificial intelligence in the future. |
Keywords
» Artificial intelligence » Distillation » Optimization » Reinforcement learning
