MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning

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, enhancing the agent's ability to handle complex tasks by leveraging modular expert learning to avoid gradient conflicts. Furthermore, MENTOR introduces a task-oriented perturbation mechanism, which heuristically samples perturbation candidates containing task-relevant information, leading to more targeted and effective optimization. MENTOR outperforms stateof-the-art methods across three simulation domains--DeepMind Control Suite, Meta-World, and Adroit. Additionally, MENTOR achieves an average of 83% success rate on three challenging real-world robotic manipulation tasks including Peg Insertion, Cable Routing, and Tabletop Golf, which significantly surpasses the success rate of 32% from the current strongest 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 mentor. Figure 1: MENTOR is validated in real-world tasks. We design three challenging robotic learning tasks for the agent to acquire skills through real-world visual reinforcement learning. MENTOR achieves the most efficient and robust policies compared to the baselines. Despite substantial progress in this field (Kostrikov et al., 2020; Yarats et al., 2021; Schwarzer et al., 2020; Stooke et al., 2021; Laskin et al., 2020a), these methods still suffer from low sample efficiency.