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%252C+has+shown+theoretical+and+empirical+successes.+However%252C+its+practical+application+in+straightforward+on-policy+actor-critic+settings+remains+surprisingly+underexplored.+We+hypothesise+that+this+is+due+to+the+difficulty+of+managing+the+entropy+reward+in+practice.+This+paper+proposes+a+simple+method+of+separating+the+entropy+objective+from+the+MaxEnt+RL+objective%252C+which+facilitates+the+implementation+of+MaxEnt+RL+in+on-policy+settings.+Our+empirical+evaluations+demonstrate+that+extending+Proximal+Policy+Optimisation+(PPO)+and+Trust+Region+Policy+Optimisation+(TRPO)+within+the+MaxEnt+framework+improves+policy+optimisation+performance+in+both+MuJoCo+and+Procgen+tasks.+Additionally%252C+our+results+highlight+MaxEnt+RL%27s+capacity+to+enhance+generalisation.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.0e6ffbe86ee257ec7e4f3e3eeeab0cb5%3frik%3d23fAYnZV7xVTng%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Maximizing+Entropy+Function+Appearance&FORM=IRPRST&ck=3F0B941E9557C64B11A8364349AFB368&selectedIndex=7&itb=0)
