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+plays+an+important+role+in+applying+RL+algorithms+to+safety-critical+real-world+applications%252C+addressing+the+trade-off+between+maximizing+rewards+and+adhering+to+safety+constraints.+This+work+introduces+a+novel+approach+that+combines+RL+with+trajectory+optimization+to+manage+this+trade-off+effectively.+Our+approach+embeds+safety+constraints+within+the+action+space+of+a+modified+Markov+Decision+Process+(MDP).+The+RL+agent+produces+a+sequence+of+actions+that+are+transformed+into+safe+trajectories+by+a+trajectory+optimizer%252C+thereby+effectively+ensuring+safety+and+increasing+training+stability.+This+novel+approach+excels+in+its+performance+on+challenging+Safety+Gym+tasks%252C+achieving+significantly+higher+rewards+and+near-zero+safety+violations+during+inference.+The+method%27s+real-world+applicability+is+demonstrated+through+a+safe+and+effective+deployment+in+a+real+robot+task+of+box-pushing+around+obstacles.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.660845f1e58b7ba424284f2bf6036e71%3frik%3dGviiLTpRclEuBw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=MDP+Reinforcement+Learning+Examples+in+Insurance+Industry+Examples&FORM=IRPRST&ck=170A30F27AE36D46B0571402AA7CF28F&selectedIndex=14&itb=0)
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