This paper aims to explore the potential of combining Deep Reinforcement Learning (DRL) with Knowledge Distillation (KD) by distilling various DRL algorithms and studying their distillation effects. By doing so, the computational burden of deep models could be reduced while maintaining the performance. The primary objective is to provide a benchmark for evaluating the performance of different DRL algorithms that have been refined using KD techniques. By distilling these algorithms, the goal is to develop efficient and fast DRL models. This research is expected to provide valuable insights that can facilitate further advancements in this promising direction. By exploring the combination of DRL and KD, this work aims to promote the development of models that require fewer GPU resources, learn more quickly, and make faster decisions in complex environments. The results of this research have the capacity to significantly advance the field of DRL and pave the way for the future deployment of resource-efficient, decision-making intelligent systems.

翻译：本文旨在探索深度强化学习与知识蒸馏结合的可能性，通过蒸馏多种深度强化学习算法并研究其蒸馏效果，旨在降低深度模型的计算负担同时保持性能。主要目标是提供一个基准，用于评估经过知识蒸馏技术优化的不同深度强化学习算法的性能。通过蒸馏这些算法，旨在开发高效且快速的深度强化学习模型。本研究有望提供宝贵见解，推动这一潜力方向取得进一步进展。通过探索深度强化学习与知识蒸馏的结合，本研究致力于推动开发需要更少GPU资源、学习更快且在复杂环境中做出更快速决策的模型。研究成果将显著推进深度强化学习领域的发展，为未来部署资源高效、决策智能的系统铺平道路。