📄 中文摘要
深度符号强化学习结合了深度学习与符号推理的优势，旨在提升智能体在复杂环境中的决策能力。该研究提出了一种新颖的框架，通过符号表示和深度学习模型的结合，实现了更高效的学习与推理。具体而言，框架利用符号知识来指导深度学习过程，从而提高了智能体在面对稀疏奖励和复杂任务时的表现。此外，实验结果表明，该方法在多个基准任务上超越了传统强化学习算法，展示了其在解决长期依赖问题和提升样本效率方面的潜力。未来的研究方向包括进一步优化符号表示和探索更复杂的环境，以推动深度符号强化学习的应用和发展。
迈向深度符号强化学习
出处: Towards Deep Symbolic Reinforcement Learning
发布: 2026年3月16日
📄 English Summary
Towards Deep Symbolic Reinforcement Learning
Deep symbolic reinforcement learning combines the advantages of deep learning and symbolic reasoning to enhance agents' decision-making capabilities in complex environments. A novel framework is proposed that integrates symbolic representations with deep learning models, enabling more efficient learning and reasoning. Specifically, the framework leverages symbolic knowledge to guide the deep learning process, improving the agent's performance in sparse reward scenarios and complex tasks. Experimental results demonstrate that this approach outperforms traditional reinforcement learning algorithms across multiple benchmark tasks, showcasing its potential in addressing long-term dependency issues and enhancing sample efficiency. Future research directions include further optimizing symbolic representations and exploring more complex environments to advance the application and development of deep symbolic reinforcement learning.