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+is+crucial+for+deploying+RL+agents+in+real-world+applications%252C+as+it+aims+to+maximize+long-term+rewards+while+satisfying+safety+constraints.+However%252C+safe+RL+often+suffers+from+sample+inefficiency%252C+requiring+extensive+interactions+with+the+environment+to+learn+a+safe+policy.+We+propose+Efficient+Safe+Policy+Optimization+(ESPO)%252C+a+novel+approach+that+enhances+the+efficiency+of+safe+RL+through+sample+manipulation.+ESPO+employs+an+optimization+framework+with+three+modes%3a+maximizing+rewards%252C+minimizing+costs%252C+and+balancing+the+trade-off+between+the+two.+By+dynamically+adjusting+the+sampling+process+based+on+the+observed+conflict+between+reward+and+safety+gradients%252C+ESPO+theoretically+guarantees+convergence%252C+optimization+stability%252C+and+improved+sample+complexity+bounds.+Experiments+on+the+Safety-MuJoCo+and+Omnisafe+benchmarks+demonstrate+that+ESPO+significantly+outperforms+existing+primal-based+and+primal-dual-based+baselines+in+terms+of+reward+maximization+and+constraint+satisfaction.+Moreover%252C+ESPO+achieves+substantial+gains+in+sample+efficiency%252C+requiring+25--29%2525+fewer+samples+than+baselines%252C+and+reduces+training+time+by+21--38%2525.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.62a270a5e22ffb636649cdccac26dbbb%3frik%3dy%252fTOX8z3NfrC8w%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Safe+Reinforcement+Learning&FORM=IRPRST&ck=9E1F850267E3E67115562D921507A1B6&selectedIndex=17&itb=0)
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