The paper introduces a novel framework called Variational Delayed Policy Optimization (VDPO) to address the issue of learning inefficiency in reinforcement learning (RL) environments with delayed observation . This work aims to improve learning efficiency without compromising performance by reformulating delayed RL as a variational inference problem and modeling it as a two-step iterative optimization problem . While delays in RL environments, especially observation delays, have been recognized as a significant challenge affecting learning efficiency, the approach presented in the paper, VDPO, is a new framework designed to tackle this specific issue .

This paper aims to validate the scientific hypothesis related to Variational Delayed Policy Optimization (VDPO) in reinforcement learning. The hypothesis revolves around the effectiveness and efficiency of VDPO in handling delayed reinforcement learning scenarios, particularly in environments with stochastic delays . The study explores the performance of VDPO using different neural representations and investigates its robustness under stochastic delays . The research delves into the challenges and solutions associated with delayed reinforcement learning, emphasizing the importance of addressing delays in real-world complex applications like robotics, transportation systems, and financial market trading .

The paper proposes a novel framework called Variational Delayed Policy Optimization (VDPO) to address the challenges of delayed reinforcement learning (RL) efficiently . VDPO formulates the delayed RL problem as a variational inference problem, utilizing optimization tools to effectively tackle the sample complexity issue . The framework operates by alternating between two steps: learning a reference policy over a delay-free Markov Decision Process (MDP) using temporal-difference learning and imitating the behavior of the reference policy over the delayed MDP through behavior cloning . This approach significantly reduces sample complexity in high-dimensional delayed MDPs compared to traditional TD learning paradigms .

Furthermore, VDPO demonstrates consistent theoretical performance with state-of-the-art methods while improving sample efficiency . Empirical results on MuJoCo benchmarks show that VDPO achieves approximately 50% less sample usage while maintaining comparable performance levels to existing approaches . The framework effectively combines variational inference principles with reinforcement learning to enhance learning efficiency and performance in delayed settings .

In contrast to direct approaches that conduct learning in the original state space and augmentation-based approaches that augment the state space, VDPO introduces a unique iterative optimization strategy that maximizes the reference policy's performance and minimizes the KL divergence between reference and delayed policies . This iterative process, involving temporal-difference learning and behavior cloning, contributes to the improved sample efficiency and robust performance of VDPO in delayed RL scenarios .

Overall, VDPO's innovative approach of treating delayed RL as a variational inference problem allows for the utilization of advanced optimization techniques to overcome sample complexity challenges and enhance the efficiency and performance of delayed reinforcement learning . The Variational Delayed Policy Optimization (VDPO) framework introduces several key characteristics and advantages compared to previous methods in the realm of delayed reinforcement learning (RL) .

1. Variational Inference Approach: VDPO redefines the delayed RL problem as a variational inference problem, allowing for the utilization of advanced optimization techniques to address sample complexity effectively . By formulating the delayed RL problem in this manner, VDPO can leverage variational inference principles to enhance learning efficiency and performance in delayed settings .

2. Two-Step Iterative Optimization: VDPO operates through a two-step iterative optimization process. Firstly, it learns a reference policy over a delay-free Markov Decision Process (MDP) using temporal-difference learning. Secondly, it imitates the behavior of the reference policy over the delayed MDP through behavior cloning. This approach significantly reduces sample complexity in high-dimensional delayed MDPs compared to traditional TD learning paradigms .

3. Improved Sample Efficiency: VDPO demonstrates enhanced sample efficiency compared to state-of-the-art methods in delayed RL scenarios. Empirical results on MuJoCo benchmarks show that VDPO achieves approximately 50% less sample usage while maintaining comparable performance levels to existing approaches .

4. Consistent Theoretical Performance: VDPO showcases consistent theoretical performance with state-of-the-art methods while improving sample efficiency. The framework effectively combines variational inference principles with reinforcement learning to enhance learning efficiency and performance in delayed settings .

5. Iterative Optimization Strategy: VDPO introduces a unique iterative optimization strategy that maximizes the reference policy's performance and minimizes the KL divergence between reference and delayed policies. This iterative process, involving temporal-difference learning and behavior cloning, contributes to the improved sample efficiency and robust performance of VDPO in delayed RL scenarios .

In summary, VDPO's innovative approach of treating delayed RL as a variational inference problem, its two-step iterative optimization process, improved sample efficiency, consistent theoretical performance, and unique iterative optimization strategy set it apart from previous methods, offering a promising framework for addressing challenges in delayed reinforcement learning efficiently and effectively .

Several related research works exist in the field of reinforcement learning with delayed feedback. Noteworthy researchers in this area include A. Abdolmaleki, J. T. Springenberg, Y. Tassa, R. Munos, N. Heess, M. Riedmiller, A. Agarwal, N. Jiang, S. M. Kakade, E. Altman, P. Nain, Y. Bouteiller, S. Ramstedt, G. Beltrame, C. Pal, J. Binas, Z. Cao, H. Guo, W. Song, K. Gao, Z. Chen, L. Zhang, X. Zhang, B. Chen, M. Xu, L. Li, D. Zhao, L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, I. Mordatch, M. Fellows, A. Mahajan, T. G. Rudner, S. Whiteson, T. J. Walsh, A. Nouri, V. Firoiu, T. Ju, J. Tenenbaum, W. Wang, D. Han, X. Luo, D. Li, Y. Wang, S. Zhan, C. Huang, Z. Yang, Q. Zhu, J. Fu, K. Luo, S. Levine, M. Gheshlaghi Azar, H. J. Kappen, T. Haarnoja, A. Zhou, J. Hasbrouck, G. Saar, J. Ho, S. Ermon, S. Huang, R. F. J. Dossa, C. Ye, J. Braga, D. Chakraborty, K. Mehta, J. G. Araújo, J. Hwangbo, I. Sa, R. Siegwart, M. Hutter, P. Liotet, D. Maran, L. Bisi, M. Restelli, Z. Liu, Z. Cen, V. Isenbaev, W. Liu, S. Wu, B. Li, D. Zhao, A. R. Mahmood, D. Korenkevych, B. J. Komer, J. Bergstra, V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, M. G. Bellemare, A. A. Rusu, J. Veness, A. K. Fidjeland, G. Ostrovski, S. Nath, M. Baranwal, H. Khadilkar, G. Neumann, D. Ramachandran, E. Amir, J. Schrittwieser, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, E. Lockhart, D. Hassabis, T. Graepel, E. Schuitema, L. Buşoniu, R. Babuška, P. Jonker, N. Tishby, N. Zaslavsky, E. Todorov, T. Erez, Y. Tassa, M. Toussaint, A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, K. V. Katsikopoulos, S. E. Engelbrecht, J. Kim, H. Kim, J. Kang, J. Baek, S. Han, and many others .

The key to the solution mentioned in the paper is the development of the Variational Delayed Policy Optimization (VDPO) framework. VDPO addresses the challenge of learning efficiency in delayed reinforcement learning by formulating the problem as a variational inference problem. This framework alternates between learning a reference policy over a delay-free Markov Decision Process (MDP) using Temporal Difference (TD) learning and imitating the behavior of this reference policy over the delayed MDP through behavior cloning. By replacing the TD learning paradigm with behavior cloning in the high-dimensional delayed MDP, VDPO effectively reduces sample complexity and achieves comparable performance with state-of-the-art methods .

The experiments in the paper "Variational Delayed Policy Optimization" were designed to evaluate the proposed VDPO framework in the MuJoCo benchmark . The authors compared VDPO with existing state-of-the-art methods, including Augmented SAC (A-SAC), DC/AC, DIDA, BPQL, and AD-SAC . The hyper-parameters settings were provided in Appendix A, and the experiments focused on sample efficiency, performance under different delay settings, and an ablation study on the representation of VDPO . Each method was run over 10 random seeds, and the training curves can be found in Appendix E . The experiments aimed to demonstrate the effectiveness of VDPO in terms of sample complexity and performance, showcasing its advantages over other baselines in the MuJoCo benchmark .

The dataset used for quantitative evaluation in the study is the MuJoCo benchmark suite, which includes various environments such as Ant-v4, HalfCheetah-v4, Hopper-v4, Humanoid-v4, HumanoidStandup-v4, Pusher-v4, Reacher-v4, Swimmer-v4, and Walker2d-v4 . The code for the Variational Delayed Policy Optimization (VDPO) framework is open-source and available for public access .

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The paper explores delayed reinforcement learning techniques in various real-world applications such as robotics, transportation systems, and financial market trading . The experiments demonstrate the effectiveness of different approaches, including direct and augmentation-based methods, in handling delays in reinforcement learning .

The direct approaches, which operate in the original state space, show high learning efficiency but may suffer from performance degradation due to the absence of the Markovian property caused by delays . On the other hand, the augmentation-based approach, which augments the state space with actions related to delays, successfully retrieves the Markovian property and enables reinforcement learning techniques over the delayed Markov Decision Process (MDP) . However, this approach works in a larger state space, leading to learning inefficiency .

To address the challenges of the augmentation-based approach, techniques like DC/AC and DIDA have been developed to accelerate the learning process and generalize pre-trained policies into augmented policies . Recent advancements have focused on evaluating augmented policies using non-augmented Q-functions to enhance learning efficiency . Additionally, approaches like ADRL propose introducing auxiliary delayed tasks to balance learning efficiency and performance in stochastic MDPs .

Overall, the experiments and results in the paper provide a comprehensive analysis of delayed reinforcement learning techniques, showcasing their effectiveness, challenges, and potential solutions in addressing delays in various complex real-world applications .

The paper makes several contributions in the field of reinforcement learning with delayed feedback:

• It discusses the challenges of delayed reinforcement learning in real-world applications like robotics, transportation systems, and financial market trading .
• The paper explores two main approaches in delayed reinforcement learning: direct approaches that learn in the original state space and augmentation-based approaches that augment the state with actions related to delays to maintain the Markovian property .
• It highlights the trade-offs between direct approaches, which offer high learning efficiency but may suffer from performance drops, and augmentation-based approaches, which can retrieve the Markovian property but face challenges due to the curse of dimensionality in a larger state space .
• The paper discusses techniques like DC/AC and DIDA that aim to improve learning efficiency in augmentation-based approaches by leveraging multi-step off-policy techniques and dataset aggregation .
• Recent advancements, such as evaluating augmented policies with non-augmented Q-functions and introducing auxiliary delayed tasks for stochastic MDPs, are also addressed in the paper .
• The conceptualization of reinforcement learning as an inference problem is highlighted as a recent trend that allows for the adaptation of optimization strategies to enhance the efficiency of reinforcement learning algorithms .

To delve deeper into the field of delayed reinforcement learning, further exploration can be conducted on the following aspects based on the provided context :

• Investigating the sample complexity issue in delayed reinforcement learning and exploring methods to enhance learning efficiency without compromising performance.
• Continuing research on Variational Delayed Policy Optimization (VDPO) as a novel framework for delayed RL, focusing on its effectiveness in resolving sample complexity problems by formulating the delayed RL problem as a variational inference problem.
• Exploring the augmentation-based approach in delayed RL to address the absence of the Markovian property caused by delays and enabling RL techniques over the delayed Markov Decision Process (MDP).
• Further research on developing auxiliary tasks with changeable delays to balance learning efficiency and performance degradation in stochastic MDPs.
• Studying the adaptation of optimization techniques to enhance RL efficiency by conceptualizing RL as an inference problem, as this approach has shown promise in improving learning efficiency in recent studies.