Abstract
In cognitive electronic warfare, when a typical combat vehicle, such as an unmanned combat air vehicle (UCAV), uses radar sensors to explore an unknown space, the target-searching fails due to an inefficient servoing/tracking system. Thus, to solve this problem, we developed an autonomous reasoning search method that can generate efficient decision-making actions and guide the UCAV as early as possible to the target area. For high-dimensional continuous action space, the UCAV’s maneuvering strategies are subject to certain physical constraints. We first record the path histories of the UCAV as a sample set of supervised experiments and then construct a grid cell network using long short-term memory (LSTM) to generate a new displacement prediction to replace the target location estimation. Finally, we enable a variety of continuous-control-based deep reinforcement learning algorithms to output optimal/sub-optimal decision-making actions. All these tasks are performed in a three-dimensional target-searching simulator, i.e., the Explorer game. Please note that we use the behavior angle (BHA) for the first time as the main factor of the reward-shaping of the deep reinforcement learning framework and successfully make the trained UCAV achieve a 99.96% target destruction rate, i.e., the game win rate, in a 0.1 s operating cycle.
Highlights
The cognitive degree of electronic warfare depends on the adaptability of the autonomous decision-making system of combat vehicles to various tasks
When using distance-based rewards, the soft actor-critic (SAC) algorithm has a certain improvement in the win rate (WR) of all levels of tasks, and the performance of the policy optimization (PPO) (KL) and PPO (CLIP) algorithms changes drastically, while the performances of the A3C and deep deterministic policy gradient (DDPG) algorithms hardly change
The behavior angle (BHA)-driven incentive enabled the performance of the deep reinforcement learning (DRL) framework to be state-of-the-art because in the most difficult game, algorithms including SAC show an obvious convergence trend, and the PPO algorithm even reaches 88.68% WR in 8 hours
Summary
The cognitive degree of electronic warfare depends on the adaptability of the autonomous decision-making system of combat vehicles to various tasks. The method proposed in this paper for UCAV control is applicable to conventional UAVs. For factor 2, because all states and behaviors of the UCAV in Explorer can be characterized and the simulation environment can provide rich and direct multiagent interactions, we consider using deep reinforcement learning (DRL) algorithms to obtain the optimal control strategy that is approximately end-to-end for the UCAV. For factor 2, because all states and behaviors of the UCAV in Explorer can be characterized and the simulation environment can provide rich and direct multiagent interactions, we consider using deep reinforcement learning (DRL) algorithms to obtain the optimal control strategy that is approximately end-to-end for the UCAV At this time, the input of the system is the UCAV’s observation state, and the output is the planned acceleration vector of the UCAV.
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