首页|Recent Research from Guangxi University Highlight Findings in Robotics and Autom ation (Sc-airl: Share-critic In Adversarial Inverse Reinforcement Learning for L ong-horizon Task)
Recent Research from Guangxi University Highlight Findings in Robotics and Autom ation (Sc-airl: Share-critic In Adversarial Inverse Reinforcement Learning for L ong-horizon Task)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Robotics - Robotics and Automation. Accordingto news originating from Nanning, People’s Republic of China, by NewsRx correspondents, research stated,“Adversar ial Inverse Reinforcement Learning (AIRL) has gained popularity as an alternativ e to supervisedimitation learning, addressing the distributional bias issue of the latter. However, it still faces significantchallenges in long-horizon tasks due to the lack of effective exploration.”
NanningPeople’s Republic of ChinaAsi aRobotics and AutomationRoboticsEmerging TechnologiesMachine LearningR einforcement LearningGuangxi University
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