MO-Playground: ロボティクスのための大規模並列多目的強化学習
MO-Playground: Massively Parallelized Multi-Objective Reinforcement Learning for Robotics
GPU上で高速に動作する多目的強化学習アルゴリズムと環境群を開発し、従来比25〜270倍の高速化でパレート最適方策を学習可能にした。
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著者: Neil Janwani, Ellen Novoseller, Vernon J. Lawhern, Maegan Tucker
分類: cs.RO
原文アブストラクト
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, resulting in vastly increased computation time. Ultimately, this has limited MORL's application 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 25-270x speed-ups compared to legacy CPU-based approaches whilst achieving superior Pareto front hypervolumes. We demonstrate the versatility of our approach by implementing a custom BRUCE humanoid robot environment using MO-Playground and learning Pareto-optimal locomotion policies across 6 realistic objectives for BRUCE, such as smoothness, efficiency and arm swinging.
