Generative adversarial imitation learning(GAIL)directly imitates the behavior of experts from human demonstration instead of designing explicit reward signals like reinforcement learning.Meanwhile,GAIL overcomes the d...Generative adversarial imitation learning(GAIL)directly imitates the behavior of experts from human demonstration instead of designing explicit reward signals like reinforcement learning.Meanwhile,GAIL overcomes the defects of traditional imitation learning by using a generative adversary network framework and shows excellent performance in many fields.However,GAIL directly acts on immediate rewards,a feature that is reflected in the value function after a period of accumulation.Thus,when faced with complex practical problems,the learning efficiency of GAIL is often extremely low and the policy may be slow to learn.One way to solve this problem is to directly guide the action(policy)in the agents'learning process,such as the control sharing(CS)method.This paper combines reinforcement learning and imitation learning and proposes a novel GAIL framework called generative adversarial imitation learning based on control sharing policy(GACS).GACS learns model constraints from expert samples and uses adversarial networks to guide learning directly.The actions are produced by adversarial networks and are used to optimize the policy and effectively improve learning efficiency.Experiments in the autonomous driving environment and the real-time strategy game breakout show that GACS has better generalization capabilities,more efficient imitation of the behavior of experts,and can learn better policies relative to other frameworks.展开更多
We propose a new framework for entity and event extraction based on generative adversarial imitation learning-an inverse reinforcement learning method using a generative adversarial network(GAN).We assume that instanc...We propose a new framework for entity and event extraction based on generative adversarial imitation learning-an inverse reinforcement learning method using a generative adversarial network(GAN).We assume that instances and labels yield to various extents of difficulty and the gains and penalties(rewards)are expected to be diverse.We utilize discriminators to estimate proper rewards according to the difference between the labels committed by the ground-truth(expert)and the extractor(agent).Our experiments demonstrate that the proposed framework outperforms state-of-the-art methods.展开更多
基金Supported in Part by the National Natural Science Foundation of China (U1808206)。
文摘Generative adversarial imitation learning(GAIL)directly imitates the behavior of experts from human demonstration instead of designing explicit reward signals like reinforcement learning.Meanwhile,GAIL overcomes the defects of traditional imitation learning by using a generative adversary network framework and shows excellent performance in many fields.However,GAIL directly acts on immediate rewards,a feature that is reflected in the value function after a period of accumulation.Thus,when faced with complex practical problems,the learning efficiency of GAIL is often extremely low and the policy may be slow to learn.One way to solve this problem is to directly guide the action(policy)in the agents'learning process,such as the control sharing(CS)method.This paper combines reinforcement learning and imitation learning and proposes a novel GAIL framework called generative adversarial imitation learning based on control sharing policy(GACS).GACS learns model constraints from expert samples and uses adversarial networks to guide learning directly.The actions are produced by adversarial networks and are used to optimize the policy and effectively improve learning efficiency.Experiments in the autonomous driving environment and the real-time strategy game breakout show that GACS has better generalization capabilities,more efficient imitation of the behavior of experts,and can learn better policies relative to other frameworks.
文摘We propose a new framework for entity and event extraction based on generative adversarial imitation learning-an inverse reinforcement learning method using a generative adversarial network(GAN).We assume that instances and labels yield to various extents of difficulty and the gains and penalties(rewards)are expected to be diverse.We utilize discriminators to estimate proper rewards according to the difference between the labels committed by the ground-truth(expert)and the extractor(agent).Our experiments demonstrate that the proposed framework outperforms state-of-the-art methods.
