Scientists are tackling the persistent challenge of controlling complex systems with numerous degrees of freedom, a key hurdle in fields ranging from robotics to biomechanics. Yunyue Wei, Chenhui Zuo, and Yanan Sui, all from Tsinghua University, alongside their colleagues, present a novel reinforcement learning approach called -Guided Flow Exploration (Qflex) that directly addresses this issue. Qflex distinguishes itself by enabling effective exploration within the full, high-dimensional action space, avoiding the limitations of dimensionality reduction techniques. This research is significant because it demonstrates substantially improved performance on standard benchmarks and, crucially, successfully controls a highly complex, full-body human musculoskeletal model,…