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Department of Computer Engineering, Dong-A University, Busan 49315, Republic of Korea
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By dynamically tuning the discount factor based on policy entropy, RL agents can learn more stably and efficiently in complex robotic tasks, outperforming traditional fixed-discount approaches by 11%.
