Mujoco Continuous Control Benchmark Tasks

该数据集包含六个连续控制基准任务，这些任务在Mujoco模拟器中进行评估，用于检验所提出的MB-MPO方法的性能。这些任务涵盖了蚂蚁、跳虫、walker2D、模拟PR2和游泳者等环境，并且在与模型无关方法相比，MB-MPO方法在样本复杂度上表现出高效性，仅用十分之一到百分之一的数据量就实现了最大性能。这些数据集展示了在机器人控制任务中的强化学习成效。

This dataset contains six continuous control benchmark tasks evaluated in the Mujoco simulator for verifying the performance of the proposed MB-MPO method. These tasks cover environments including Ant, Hopper, Walker2D, simulated PR2, and Swimmer. Compared with model-free methods, the MB-MPO method exhibits high efficiency in terms of sample complexity, achieving maximum performance with only one-tenth to one-hundredth of the data volume. This dataset demonstrates the effectiveness of reinforcement learning in robotic control tasks.