介绍一种批评驱动Voronoi量化方法，实现深度强化学习策略向可解释模型的高效蒸馏，解决性能-可解释性权衡难题。
arXiv:2605.14897v1 Announce Type: cross Abstract: Despite many successful attempts at explaining Deep Reinforcement Learning policies using distillati…
系统介绍深度强化学习与模仿学习，面向具身智能体复杂决策问题，是入门该领域的高质量综述。
arXiv:2512.08052v3 Announce Type: replace-cross Abstract: Embodied agents, such as robots and virtual characters, must continuously select actions to …
经典Double Q-learning的深度强化学习新范式，通过完全解耦动作选择与评估彻底消除最大化偏差。
arXiv:2507.00275v2 Announce Type: replace-cross Abstract: Double Q-learning is a classical control algorithm that mitigates the maximization bias of Q…
OpenAI首次深度强化学习教育工作坊回顾，亮点在于Spinning Up项目的实践教学与社区反响。
On February 2, we held our first Spinning Up Workshop as part of our new education initiative at OpenAI.