Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。 今天的主题是：Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement LearningSource: Ding et al., "Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning" (arXiv:2402.03570v4) Main Themes: Compounding errors in long-horizon prediction: Traditional one-step dynamics models suffer from accumulating errors when rolled out over long horizons. Leveraging sequence modeling for multi-step prediction: The paper proposes Diffusion World Model (DWM) as a conditional diffusion model that predicts multiple future states and rewards concurrently, mitigating compounding errors. Offline reinforcement learning: DWM is applied in offline RL to learn policies from static datasets without online interaction.Key Ideas and Facts: DWM outperforms one-step models in long-horizon planning:DWM exhibits robustness to long-horizon simulation, maintaining consistent performance even with a horizon of 31 steps, unlike one-step models which show performance degradation. "DWM-TD3BC and DWM-IQL maintain relatively high returns without significant performance degradation, even using horizon length 31." This robustness is attributed to DWM's ability to generate entire trajectories, reducing error accumulation compared to recursive one-step predictions. DWM acts as value regularization in offline RL:DWM, trained solely on offline data, can be interpreted as a representation of the behavior policy that generated the data. Integrating DWM into value estimation acts as a form of value regularization, preventing the policy from exploiting erroneous values for out-of-distribution actions. DWM offers computational advantages over Decision Diffuser (DD):Unlike DD, which needs to generate the entire trajectory at inference time, DWM only intervenes in critic training. This makes DWM-based policies more efficient to execute, as the world model doesn't need to be invoked during action generation. "This means, at inference time, DD needs to generate the whole trajectory, which is computationally expensive." DWM-based algorithms are comparable to model-free counterparts:DWM-based algorithms like DWM-TD3BC and DWM-IQL achieve performance comparable to, or even slightly exceeding, their model-free counterparts (TD3+BC and IQL) on the D4RL dataset. Key architectural choices:DWM employs a temporal U-net architecture for noise prediction, conditioned on the initial state, action, and target return. Classifier-free guidance is used to enhance the influence of the target return during training. Stride sampling is applied to accelerate the inference process.Important Quotes: Compounding Errors: "When planning for multiple steps into the future, pone is recursively invoked, leading to a rapid accumulation of errors and unreliable predictions for long-horizon rollouts." DWM for Multi-step Prediction: "Conditioning on current state st, action at, and expected return gt, DWM simultaneously predicts multistep future states and rewards." Value Regularization: "As the DWM is trained exclusively on offline data, it can be seen as a synthesis of the behavior policy that generates the offline dataset. In other words, diffusion-MVE introduces a type of value regularization for offline RL through generative modeling." Efficiency Compared to DD: "Our approach, instead, can connect with any MF offline RL methods that is fast to execute for inference."Overall, the paper presents DWM as a promising approach for mitigating compounding errors in long-horizon prediction and improving offline reinforcement learning. It offers a robust and computationally efficient alternative to traditional one-step dynamics models and showcases competitive performance against model-free methods. Further research is warranted to explore the full potential of DWM in various RL applications. 原文链接：https://arxiv.org/a