Summary of Transductive Off-policy Proximal Policy Optimization, by Yaozhong Gan et al.
Transductive Off-policy Proximal Policy Optimization
by Yaozhong Gan, Renye Yan, Xiaoyang Tan, Zhe Wu, Junliang Xing
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Transductive Off-policy PPO (ToPPO), an extension to the popular Proximal Policy Optimization (PPO) model-free reinforcement learning algorithm. Unlike original PPO, which is on-policy and constrained by its data source, ToPPO can harness off-policy data, providing improved performance and versatility. Theoretical justifications for incorporating off-policy data in PPO training are presented, along with guidelines for safe application. A novel formulation of the policy improvement lower bound for prospective policies derived from off-policy data is introduced, accompanied by a computationally efficient optimization mechanism ensuring monotonic improvement. Experimental results across six tasks demonstrate ToPPO’s promising performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ToPPO is an updated version of PPO that can use information from other sources. Currently, PPO only uses its own experiences to learn and improve. This new method lets it use data collected by others, making it more powerful and flexible. The researchers explain why this change makes sense and provide rules for using the new technique safely. They also introduce a way to calculate an improvement bound that ensures the algorithm gets better over time. Tests on six different tasks show that ToPPO can do well. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
