Summary of Simudice: Offline Policy Optimization Through World Model Updates and Dice Estimation, by Catalin E. Brita et al.
SimuDICE: Offline Policy Optimization Through World Model Updates and DICE Estimation
by Catalin E. Brita, Stephan Bongers, Frans A. Oliehoek
First submitted to arxiv on: 9 Dec 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 This paper proposes a framework called SimuDICE for offline reinforcement learning, which tackles the challenges of deriving an effective policy from a pre-collected set of experiences. The authors address the distribution mismatch between the target policy and the behavioral policy used to collect the data, as well as the limited sample size. By iteratively refining the initial policy using synthetically generated experiences from a learned dynamic model of the environment, SimuDICE enhances the quality of these simulated experiences. This approach guides policy improvement by balancing experiences similar to those frequently encountered with ones that have a distribution mismatch. The paper demonstrates that SimuDICE achieves performance comparable to existing algorithms while requiring fewer pre-collected experiences and planning steps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning is challenging because it’s hard to get the right policy from just some old data. This is because there can be a big difference between what you want the policy to do and how the data was collected in the first place. To make things better, the authors introduce a new way of doing things called SimuDICE. It uses a special model that helps generate fake experiences based on what it knows about the environment. Then, it adjusts these fake experiences to make them more like the real ones you would encounter. This helps get the policy better without needing as much old data or planning time. |
Keywords
» Artificial intelligence » Reinforcement learning
