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A dual-actor proximal policy optimization algorithm for humanoid robot navigation control

delete2026-03-27
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PRE
AI
J
Jacky Baltes
I
Ilham Akbar
S
Saeed Saeedvand *
DOI：10.1016/j.asoc.2026.115093delete
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Abstract

Abstract

En 中文
• Introducing a Dual-Actor Proximal Policy Optimization (DA-PPO) algorithm. • Underscoring the potential of DA-PPO in robotic applications on a popular humanoid robot. • Enhancing model-free RL techniques’ performance for complex humanoid robot control. • Compare against four algorithms: PPO, DDPG, TD3, and SAC.
Keywords:
Dual-Actor Proximal Policy Optimization
Humanoid Robot Navigation
Model-Free Reinforcement Learning
Policy Optimization
Robotics Control

Journal

Applied Soft Computing cover
Applied Soft Computing
IF:
6.6
Papers:
1.4W
Citations:
4.8W

Organization

No organization information available