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This work was supported by the Institute of Information Communications Technology Planning Evaluation (IITP) funded by the Korea government under Grant 2022-0-00469 and the BK21 FOUR from the Ministry of Education (Republic of Korea).Taeho Lee and Donghwan Lee are with the School of Electrical Engineering, Korea Advanced Institute of Science and Technology, , OH 45435, USA eho0228,@kaist.ac.kr,donghwan,@kaist.ac.kr
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Make your RL agents disturbance-proof: a fractional objective stabilizes minimax training, leading to policies that shrug off external forces and model uncertainties.