Summary of Diffusion World Model: Future Modeling Beyond Step-by-step Rollout For Offline Reinforcement Learning, by Zihan Ding et al.
Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning
by Zihan Ding, Amy Zhang, Yuandong Tian, Qinqing Zheng
First submitted to arxiv on: 5 Feb 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 DWM introduces a conditional diffusion model that predicts multiple future steps and rewards simultaneously, eliminating the need for recursive queries. This allows for long-horizon predictions in a single pass. The paper integrates DWM into model-based value estimation, where short-term returns are simulated by future trajectories sampled from DWM. In offline reinforcement learning, DWM can be seen as a conservative value regularization through generative modeling or as a data source enabling offline Q-learning with synthetic data. Experiments on the D4RL dataset confirm the robustness of DWM to long-horizon simulation, achieving a 44% performance gain compared to one-step dynamics models and comparable results to model-free counterparts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DWM is a new way for computers to predict what will happen in the future. It can look ahead many steps instead of just one step at a time. This helps with learning and decision-making. DWM combines with other ideas to estimate how good something is going to be based on possible futures. It’s like having a crystal ball that shows you different possibilities, and you can use this information to make better choices. The test results show that DWM works well and performs much better than some existing methods. |
Keywords
* Artificial intelligence * Diffusion model * Regularization * Reinforcement learning * Synthetic data
