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arXiv · 2505.22642

FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control

Younggyo Seo·Carmelo Sferrazza·Haoran Geng·Michal Nauman·Zhao-Heng Yin·Pieter Abbeel
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Abstract

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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Younggyo Seo, Carmelo Sferrazza, Haoran Geng, Michal Nauman, Zhao-Heng Yin, Pieter Abbeel. 2025-06-01. FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control. https://arxiv.org/abs/2505.22642

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