用专家混合网络和任务扰动提升视觉强化学习样本效率
MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning
- 采用专家混合架构替代传统MLP，动态激活不同子网络
- 在三个仿真基准上超越当前最优，在真实机器人任务中达83%成功率
- 适合关注机器人视觉决策与高效训练的科研人员
视觉深度强化学习使机器人能从视觉输入中习得处理非结构化任务的技能，但现有算法存在样本效率低的问题，限制了实际应用。本文提出MENTOR方法，通过将标准多层感知机替换为专家混合（MoE）主干网络，并引入任务导向扰动机制，同时优化模型架构与训练策略。在三个仿真基准上，MENTOR性能优于当前最先进方法；在三个高难度真实世界机器人操作任务中，平均成功率达83%，显著超过现有最强无模型视觉强化学习算法的32%成功率。结果表明，提升样本效率对推动视觉强化学习在真实机器人中的应用至关重要。实验视频见https://suninghuang19.github.io/mentor_page/。
原文摘要 · Abstract (English)
Visual deep reinforcement learning (RL) enables robots to acquire skills from visual input for unstructured tasks. However, current algorithms suffer from low sample efficiency, limiting their practical applicability. In this work, we present MENTOR, a method that improves both the architecture and optimization of RL agents. Specifically, MENTOR replaces the standard multi-layer perceptron (MLP) with a mixture-of-experts (MoE) backbone and introduces a task-oriented perturbation mechanism. MENTOR outperforms state-of-the-art methods across three simulation benchmarks and achieves an average of 83% success rate on three challenging real-world robotic manipulation tasks, significantly surpassing the 32% success rate of the strongest existing model-free visual RL algorithm. These results underscore the importance of sample efficiency in advancing visual RL for real-world robotics. Experimental videos are available at https://suninghuang19.github.io/mentor_page/.
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