在人工智能领域中，强化学习已成为一个研究焦点，在多个应用场景中取得了显著成就。然而，强化学习在实际部署经常面临延迟问题的挑战，该问题破坏了强化学习的基础假设——马尔可夫性质，导致在有延迟环境下强化学习算法的性能大幅下降，甚至完全失效。针对这一问题，开发能够适应延迟环境的强化学习算法，成为当前研究的迫切需求。本研究提出了一种创新的方法论，即通过融合生成对抗模型来充分利用有限的无延迟环境下的专家知识，提升强化学习算法在有延迟环境下的表现。本研究设计了一种结合了生成对抗模型和传统强化学习优势的算法，通过对判别器网络和强化学习策略的有效训练，从少量无延迟专家演示中生成大量高质量的延迟环境样本，进而使得智能体能够在延迟环境中进行有效学习，显著提升其在真实延迟环境中的应用性能。本研究从以下三个维度深入探讨了生成对抗模型在强化学习中的应用：(1) 针对固定延迟问题，传统强化学习算法面临环境交互频繁的问题。为此，本研究提出了一种状态增广的对抗模仿学习算法DAIL。通过对无延迟环境的演示数据进行重排序和预处理，在对抗模仿学习框架下对策略进行训练。经过实验验证，DAIL算法不仅显著提高了采样效率，而且在多个固定延迟基准测试任务中展现出良好的性能表现。(2) 在处理随机延迟问题时，现有强化学习算法没有对随机延迟进行正面建模。为了解决这一问题，本研究提出了混合演示的对抗模仿学习算法SDAIL。通过掩码状态和设计损失函数，该算法显式地对随机延迟信息进行了建模。实验结果表明，SDAIL算法在多个随机延迟测试基准上均展现出了良好的性能，证明了其对随机性的鲁棒性。(3) 针对广告工业场景中带有延迟的实时竞价广告决策任务，本研究提出了一种基于随机延迟对抗模仿学习的决策系统。该系统引入两种创新的采样技术，并结合对抗模仿学习框架，有效地解决了实时竞价广告决策任务。经过在真实工业数据集上的测试，该系统不仅证明其良好的测试效果，而且达到高效的收敛速率。综上所述，本研究通过引入生成对抗网络，为强化学习在延迟环境下的应用提供了一种新的方法论视角和实际可行的解决方案。

In the field of artificial intelligence, reinforcement learning has emerged as a focal point of research and has achieved notable successes across multiple application scenarios. However, the deployment of reinforcement learning often encounters the challenge of delays, which disrupts the foundational assumption of Markov properties. This leads to a significant degradation in the performance of traditional reinforcement learning algorithms in delayed environments, and in some cases, complete failure. Addressing this issue by developing reinforcement learning algorithms that can adapt to delayed environments has become an urgent need in contemporary research.This study proposes an innovative methodology, namely the integration of Generative Adversarial Networks (GANs), to fully leverage the expertise available from limited non-delayed environments, aiming to enhance the performance of reinforcement learning algorithms in delayed settings. We have designed an algorithm that merges the advantages of generative adversarial models and traditional reinforcement learning. Through effective training of discriminator and generator networks, this algorithm generates a large volume of high-quality delayed environment samples from a small set of non-delayed expert demonstrations, enabling the agent to engage in effective learning within a simulated delayed environment, and significantly improving its performance in real-world delayed settings.This research delves into the application of generative adversarial models in reinforcement learning from the following three dimensions:(1) Addressing the issue of frequent environmental interactions faced by traditional reinforcement learning algorithms in fixed delay problems, this study introduces a state-augmented adversarial imitation learning algorithm, DAIL (\textbf{D}elayed \textbf{A}dversarial \textbf{I}mitation \textbf{L}earning). This algorithm meticulously reorders and preprocesses demonstration data from non-delayed environments and trains policies within an adversarial imitation learning framework. Rigorous experimental validation has shown that the DAIL algorithm not (2) When dealing with stochastic delay problem, the existing reinforcement learning algorithm does not model the stochastic delay positively. To address this, this study proposes a mixed demonstration adversarial imitation learning algorithm, SDAIL (\textbf{S}tochastic \textbf{D}elayed \textbf{A}dversarial \textbf{I}mitation \textbf{L}earning). By integrating masked states and specially designed loss functions, this algorithm explicitly models stochastic delay information. Experimental results indicate that the SDAIL algorithm exhibits robust performance across various stochastic delay benchmarks, proving its high resilience to randomness.(3) Aiming at the real-time bidding advertising decision task with delay in the advertising industry scene, this paper proposes a framework of advertising decision making based on stochastic delay adversarial imitation learning algorithm. The framework is based on stochastic delay adversarial imitation learning algorithm and introduces two innovative sampling techniques to effectively solve the real-time bidding advertising decision task. After testing on real industrial data sets, the framework not only proves its good test results, but also achieves high convergence rate.In summary, this research, through the introduction of generative adversarial models, offers a novel methodological perspective and practical solution for the application of reinforcement learning in delayed environments.only significantly enhances sampling efficiency but also demonstrates exceptional performance across multiple fixed delay benchmarks.