arXiv:2603.09237 · v2
MO-Playground: Massively Parallelized Multi-Objective Reinforcement Learning for Robotics
摘要
Multi-objective reinforcement learning (MORL) is a powerful tool to learn Pareto-optimal policy families across conflicting objectives. However, unlike traditional RL algorithms, existing MORL algorithms do not effectively leverage large-scale parallelization to concurrently simulate thousands of environments, thus facing vastly increased computation time. Ultimately, this has limited the application of MORL towards complex multi-objective robotics problems. To address these challenges, we present 1) MORLAX, a new GPU-native, fast MORL algorithm, and 2) MO-Playground, a pip-installable playground of GPU-accelerated multi-objective environments. Together, MORLAX and MO-Playground approximate Pareto sets within minutes, offering 26-271x speed-ups compared to legacy CPU-based approaches and up to 19x speed-ups over prior GPU-based approaches whilst learning superior Pareto front hypervolumes. We demonstrate MO-Playground's versatility by implementing a custom BRUCE humanoid robot environment and learning Pareto-optimal locomotion policies across 6 practical objectives in simulation, such as smoothness, efficiency and arm swinging. Our toolbox is available at https://dynamicmobility.github.io/moplayground/.
作者备注：8 pages, 4 figures, 2 tables
期刊信息：N. Janwani, E. Novoseller, V. J. Lawhern and M. Tucker, "MO-Playground: Massively Parallelized Multi-Objective Reinforcement Learning for Robotics," in IEEE Robotics and Automation Letters, vol. 11, no. 8, pp. 9431-9438, Aug. 2026