FastTD3让机器人行走训练快至3小时，代码轻便易用。
FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control
- 基于TD3改进：并行仿真+大批次更新+分布评分类
- 单卡A100 3小时内完成多种人形机器人任务
- 适合想快速验证强化学习算法的机器人研究者
强化学习在机器人领域取得显著进展，但其复杂性和长训练周期仍是主要瓶颈。本文提出FastTD3，一种简单、快速且高效的人形机器人控制强化学习算法，适用于HumanoidBench、IsaacLab和MuJoCo Playground等主流环境。该方法通过在离策略TD3基础上引入并行仿真、大批次更新、分布评分类及精心调优的超参数，实现高效训练。FastTD3可在单张A100 GPU上于3小时内解决多个HumanoidBench任务，且训练过程稳定。我们还提供了轻量级、易用的FastTD3实现，旨在加速机器人强化学习研究。
原文摘要 · Abstract (English)
Reinforcement learning (RL) has driven significant progress in robotics, but its complexity and long training times remain major bottlenecks. In this report, we introduce FastTD3, a simple, fast, and capable RL algorithm that significantly speeds up training for humanoid robots in popular suites such as HumanoidBench, IsaacLab, and MuJoCo Playground. Our recipe is remarkably simple: we train an off-policy TD3 agent with several modifications -- parallel simulation, large-batch updates, a distributional critic, and carefully tuned hyperparameters. FastTD3 solves a range of HumanoidBench tasks in under 3 hours on a single A100 GPU, while remaining stable during training. We also provide a lightweight and easy-to-use implementation of FastTD3 to accelerate RL research in robotics.
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