Summary of Diffusion Policies Creating a Trust Region For Offline Reinforcement Learning, by Tianyu Chen et al.
Diffusion Policies creating a Trust Region for Offline Reinforcement Learning
by Tianyu Chen, Zhendong Wang, Mingyuan Zhou
First submitted to arxiv on: 30 May 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 paper introduces a novel offline reinforcement learning approach called Diffusion Trusted Q-Learning (DTQL), which combines the expressiveness of diffusion models with the efficiency of one-step policies. DTQL eliminates the need for iterative denoising sampling, making it computationally efficient during both training and inference. The approach is evaluated against popular distillation methods in 2D bandit scenarios and gym tasks, outperforming them on most D4RL benchmark tasks while also demonstrating improved speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning uses pre-collected data to train optimal policies. A new approach called Diffusion Trusted Q-Learning (DTQL) combines the benefits of diffusion models and one-step policies to improve efficiency. DTQL eliminates the need for iterative denoising sampling, making it faster than previous methods. The approach is tested in different scenarios and shows better results than other methods while being more efficient. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Inference » Reinforcement learning
