Summary of Diffusion Spectral Representation For Reinforcement Learning, by Dmitry Shribak et al.
Diffusion Spectral Representation for Reinforcement Learning
by Dmitry Shribak, Chen-Xiao Gao, Yitong Li, Chenjun Xiao, Bo Dai
First submitted to arxiv on: 23 Jun 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 proposed algorithm, Diffusion Spectral Representation (Diff-SR), is a novel framework for reinforcement learning that leverages the expressiveness of diffusion models. By exploiting the connection between diffusion models and energy-based models, Diff-SR enables the extraction of sufficient representations for value functions in Markov decision processes (MDP) and partially observable Markov decision processes (POMDP). This approach facilitates efficient policy optimization while bypassing the computational cost associated with sampling from diffusion models. The paper demonstrates the benefits of Diff-SR through comprehensive empirical studies across various benchmarks, showing robust and advantageous performance in both fully and partially observable settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion-based models have been successful in reinforcement learning due to their ability to model complex distributions. However, they often require many iterations to generate a single sample, making them computationally expensive. This paper proposes an algorithm called Diffusion Spectral Representation (Diff-SR) that uses the connection between diffusion models and energy-based models to extract useful representations for value functions in Markov decision processes (MDPs) and partially observable MDPs (POMDPs). This approach makes it possible to optimize policies more efficiently without having to sample from the diffusion model. The paper shows that Diff-SR can deliver good results across different benchmarks. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Optimization * Reinforcement learning
