3 accepted papers
Deep reinforcement learning has gained increasing popularity in the beyond visual range engagement of unmanned combat aerial vehicles. Regardless of the success of the maneuvering decision, existing methods either utilize sparse rewards to optimize fire control or adopt a predefined fire policy, sev
Extensive utilization of deep reinforcement learning (DRL) policy networks in diverse continuous control tasks has raised questions regarding performance degradation in expansive state spaces where the input state norm is larger than that in the training environment. This paper aims to uncover the u…
Numerous algorithms have been proposed for quadrotor flight control. Conventional methods require massive labor of parameter adjustment. Deep reinforcement learning (DRL) methods also need enormous computation and complicated hyperparameter tuning, since most of them regard the quadrotor dynamics as