AI RESEARCH
FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control
arXiv CS.LG
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ArXi:2604.04539v1 Announce Type: new Reinforcement learning (RL) is a core approach for robot control when expert nstrations are unavailable. On-policy methods such as Proximal Policy Optimization (PPO) are widely used for their stability, but their reliance on narrowly distributed on-policy data limits accurate policy evaluation in high-dimensional state and action spaces.