TLDR: DmC is a new framework for cross-domain offline reinforcement learning that uses k-nearest neighbor (k-NN) estimation to measure domain differences without neural network overfitting. It then guides a diffusion model with these k-NN scores to generate more relevant source data, significantly improving policy learning and sample efficiency, especially when target data is limited.
Reinforcement Learning (RL) has shown incredible potential in solving complex real-world problems, but it often requires a vast amount of trial-and-error interactions with an environment. This can be impractical or unsafe in situations like autonomous driving or healthcare, where data collection is costly or risky. A common approach to mitigate this is cross-domain RL, where policies are trained in a safer, faster “source” environment (like a simulator) and then adapted to a “target” real-world scenario using a limited amount of real-world data.
The core challenge in cross-domain offline RL, especially when target data is scarce, is accurately identifying and utilizing source samples that are most relevant to the target domain. Existing methods often struggle with two main issues: dataset imbalance and partial domain overlap. Dataset imbalance occurs because there’s a large source dataset but only a small target dataset, which can lead to neural networks used for measuring domain differences overfitting and providing unhelpful information. Partial domain overlap means that only a portion of the source data is actually useful and closely matches the target domain.
To address these significant challenges, researchers have proposed a novel framework called DmC, which stands for Nearest Neighbor Guidance Diffusion Model for Offline Cross-domain Reinforcement Learning. DmC introduces a new way to measure how close source samples are to the target domain using a technique called k-nearest neighbor (k-NN) estimation. Unlike previous methods that rely on complex neural network training, k-NN estimation avoids overfitting, making it more reliable with limited target data.
Furthermore, DmC tackles the problem of partial domain overlap by using this k-NN-based domain proximity score to guide a diffusion model. A diffusion model is a type of generative AI that can create new data. By guiding it with the k-NN scores, DmC generates additional source samples that are better aligned with the target domain. This effectively augments the dataset with more relevant information, enhancing the learning process for the policy.
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The DmC framework integrates k-NN estimation for accurate domain gap measurement and a guided diffusion model for targeted sample generation. This combination allows it to effectively leverage source data even when target data is limited. Through extensive experiments in various simulated environments, DmC has demonstrated significant performance gains, outperforming existing state-of-the-art cross-domain offline RL methods. This approach offers a promising solution for improving sample efficiency in real-world RL applications where data collection is a major constraint. You can read the full research paper here.
