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今天的主题是：Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning

Source: 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/abs/2402.03570