It is suggested that hindsight becomes an obstacle to the objective investigation of an accident, and that the proper countermeasures for the prevention of such an accident is impossible if we view the accident with h...It is suggested that hindsight becomes an obstacle to the objective investigation of an accident, and that the proper countermeasures for the prevention of such an accident is impossible if we view the accident with hindsight. Therefore, it is important for organizational managers to prevent hindsight from occurring so that hindsight does not hinder objective and proper measures to be taken and this does not lead to a serious accident. In this study, a basic phenomenon potentially related to accidents, that is, hindsight was taken up, and an attempt was made to explore the phenomenon in order to get basically insights into the prevention of accidents caused by such a cognitive bias.展开更多
As an advanced combat weapon,Unmanned Aerial Vehicles(UAVs)have been widely used in military wars.In this paper,we formulated the Autonomous Navigation Control(ANC)problem of UAVs as a Markov Decision Process(MDP)and ...As an advanced combat weapon,Unmanned Aerial Vehicles(UAVs)have been widely used in military wars.In this paper,we formulated the Autonomous Navigation Control(ANC)problem of UAVs as a Markov Decision Process(MDP)and proposed a novel Deep Reinforcement Learning(DRL)method to allow UAVs to perform dynamic target tracking tasks in large-scale unknown environments.To solve the problem of limited training experience,the proposed Imaginary Filtered Hindsight Experience Replay(IFHER)generates successful episodes by reasonably imagining the target trajectory in the failed episode to augment the experiences.The welldesigned goal,episode,and quality filtering strategies ensure that only high-quality augmented experiences can be stored,while the sampling filtering strategy of IFHER ensures that these stored augmented experiences can be fully learned according to their high priorities.By training in a complex environment constructed based on the parameters of a real UAV,the proposed IFHER algorithm improves the convergence speed by 28.99%and the convergence result by 11.57%compared to the state-of-the-art Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm.The testing experiments carried out in environments with different complexities demonstrate the strong robustness and generalization ability of the IFHER agent.Moreover,the flight trajectory of the IFHER agent shows the superiority of the learned policy and the practical application value of the algorithm.展开更多
文摘It is suggested that hindsight becomes an obstacle to the objective investigation of an accident, and that the proper countermeasures for the prevention of such an accident is impossible if we view the accident with hindsight. Therefore, it is important for organizational managers to prevent hindsight from occurring so that hindsight does not hinder objective and proper measures to be taken and this does not lead to a serious accident. In this study, a basic phenomenon potentially related to accidents, that is, hindsight was taken up, and an attempt was made to explore the phenomenon in order to get basically insights into the prevention of accidents caused by such a cognitive bias.
基金co-supported by the National Natural Science Foundation of China(Nos.62003267 and 61573285)the Natural Science Basic Research Plan in Shaanxi Province of China(No.2020JQ-220)+1 种基金the Open Project of Science and Technology on Electronic Information Control Laboratory,China(No.JS20201100339)the Open Project of Science and Technology on Electromagnetic Space Operations and Applications Laboratory,China(No.JS20210586512).
文摘As an advanced combat weapon,Unmanned Aerial Vehicles(UAVs)have been widely used in military wars.In this paper,we formulated the Autonomous Navigation Control(ANC)problem of UAVs as a Markov Decision Process(MDP)and proposed a novel Deep Reinforcement Learning(DRL)method to allow UAVs to perform dynamic target tracking tasks in large-scale unknown environments.To solve the problem of limited training experience,the proposed Imaginary Filtered Hindsight Experience Replay(IFHER)generates successful episodes by reasonably imagining the target trajectory in the failed episode to augment the experiences.The welldesigned goal,episode,and quality filtering strategies ensure that only high-quality augmented experiences can be stored,while the sampling filtering strategy of IFHER ensures that these stored augmented experiences can be fully learned according to their high priorities.By training in a complex environment constructed based on the parameters of a real UAV,the proposed IFHER algorithm improves the convergence speed by 28.99%and the convergence result by 11.57%compared to the state-of-the-art Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm.The testing experiments carried out in environments with different complexities demonstrate the strong robustness and generalization ability of the IFHER agent.Moreover,the flight trajectory of the IFHER agent shows the superiority of the learned policy and the practical application value of the algorithm.
