Unmanned Combat Aerial Vehicle (UCAV) dogfight, which refers to a fight between two or more UCAVs usually at close quarters, plays a decisive role on the aerial battlefields. With the evolution of artificial intelligence, dogfight progressively transits towards intelligent and autonomous modes. However, the development of autonomous dogfight policy learning is hindered by challenges such as weak exploration capabilities, low learning efficiency, and unrealistic simulated environments. To overcome these challenges, this paper proposes a novel imitative reinforcement learning framework, which efficiently leverages expert data while enabling autonomous exploration. The proposed framework not only enhances learning efficiency through expert imitation, but also ensures adaptability to dynamic environments via autonomous exploration with reinforcement learning. Therefore, the proposed framework can learn a successful dogfight policy of 'pursuit-lock-launch' for UCAVs. To support data-driven learning, we establish a dogfight environment based on the Harfang3D sandbox, where we conduct extensive experiments. The results indicate that the proposed framework excels in multistage dogfight, significantly outperforms state-of-the-art reinforcement learning and imitation learning methods. Thanks to the ability of imitating experts and autonomous exploration, our framework can quickly learn the critical knowledge in complex aerial combat tasks, achieving up to a 100% success rate and demonstrating excellent robustness.

This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision. A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs). Double deep Q network (DDQN) is applied for BFM selection in UCAV dogfight, where the opponent strategy during the training process is constructed with DT. Our simulation result shows that, the agent can achieve a win rate of 85.75% against the DT strategy, and positive results when facing various unseen opponents. Based on the proposed frame, interpretability of the DRL-based dogfight is significantly improved. The agent performs yo-yo to adjust its turn rate and gain higher maneuverability. Emergence of "Dive and Chase" behavior also indicates the agent can generate a novel tactic that utilizes the drawback of its opponent.

AI and weapons of the future are very concerning. Since the early days of computing, scientists have been exploring artificial intelligence's potential to impact various aspects of life. In recent years, AI has begun to play a more significant role in multiple industries, like transport, finance, and manufacturing. But as always, we can also use revolutionary technology for warfare. Also Watch: A drone that can dodge anything thrown at it.

Turkey aims to be among the first countries to have an entirely artificial intelligence (AI)-controlled unmanned warplane, with plans for it to take to the Turkish skies in 2023, President Recep Tayyip Erdoğan said Wednesday. The success of Turkish unmanned aerial vehicles (UAV) in the field has produced results that "require war strategies to be rewritten," the president said. Erdoğan was speaking at the ruling Justice and Development Party's (AK Party) parliamentary group meeting in the capital Ankara. The president added that currently a total of 180 Bayraktar TB2 unmanned combat aerial vehicles (UCAVs) are operated in four countries, including Turkey. Previously, Turkish drone magnate Baykar's Chief Technology Officer Selçuk Bayraktar said the maiden flight of the prototype of the country's domestically-made unmanned fighter jet is scheduled for 2023.

Unmanned aircraft systems can perform some more dangerous and difficult missions than manned aircraft systems. In some highly complicated and changeable tasks, such as air combat, the maneuvering decision mechanism is required to sense the combat situation accurately and make the optimal strategy in real-time. This paper presents a formulation of a 3-D one-on-one air combat maneuvering problem and an approximate dynamic programming approach for computing an optimal policy on autonomous maneuvering decision making. The aircraft learns combat strategies in a Reinforcement Leaning method, while sensing the environment, taking available maneuvering actions and getting feedback reward signals. To solve the problem of dimensional explosion in the air combat, the proposed method is implemented through feature selection, trajectory sampling, function approximation and Bellman backup operation in the air combat simulation environment. This approximate dynamic programming approach provides a fast response to a rapidly changing tactical situation, learns in long-term planning, without any explicitly coded air combat rule base.

This paper proposes a new reinforcement learning (RL) algorithm that enhances exploration by amplifying the imitation effect (AIE). This algorithm consists of self-imitation learning and random network distillation algorithms. We argue that these two algorithms complement each other and that combining these two algorithms can amplify the imitation effect for exploration. In addition, by adding an intrinsic penalty reward to the state that the RL agent frequently visits and using replay memory for learning the feature state when using an exploration bonus, the proposed approach leads to deep exploration and deviates from the current converged policy. We verified the exploration performance of the algorithm through experiments in a two-dimensional grid environment. In addition, we applied the algorithm to a simulated environment of unmanned combat aerial vehicle (UCAV) mission execution, and the empirical results show that AIE is very effective for finding the UCAV's shortest flight path to avoid an enemy's missiles.

The Air Force Research Laboratory (AFRL) recently tested autonomously flying F-16 fighter jets in collaboration with Lockheed Martin. The tests could mark a big leap for military drone technology as these jets could be used in the future for large scale air-to-ground strikes. "This demonstration is an important milestone in AFRL's maturation of technologies needed to integrate manned and unmanned aircraft in a strike package. We've not only shown how an Unmanned Combat Air Vehicle can perform its mission when things go as planned, but also how it will react and adapt to unforeseen obstacles along the way," Capt. Andrew Petry, AFRL autonomous flight operations engineer, said in a press release issued Monday by Lockheed Martin.

This November in Yemen, an unmanned Predator plane -- known as a drone -- blew up a car full of suspected Al Qaeda members. The plane's ''remote pilot'' sat in a trailer located miles out of harm's way. The Pentagon considers unmanned planes like the Predator perfect for ''the 3 D's'': missions that are so dull, dirty or dangerous that it's best to leave humans out of the equation. The Predator is just the start of what may well be the largest shift in military tactics since the invention of gunpowder -- a wholesale removal of American personnel from the front lines. This year at Edwards Air Force Base in California, the biggest advance yet in robotic warfare took its first flight: the UCAV, or Unmanned Combat Air Vehicle.

PsiberLogic is a completely free, open-source fuzzy logic controller package for Python 3. Psibernetix proudly supports the amazing Python community, and is happy to contribute to Python's open-source movement. This package is for anyone seeking a high-performance, python3-callable package for creating fuzzy logic controllers. Details on ALPHA – a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

Top Gun was released 30 years ago and it looks as if the Maverick of tomorrow will be made of microchips. Developed by a University of Cincinnati (US) doctoral candidate, an Artificial Intelligence (AI) called ALPHA has consistently beaten other AIs and a retired United States Air Force Colonel in a high-fidelity, air-combat simulator using what's known as a genetic-fuzzy system that relies on off-the-shelf PC processors to do what was thought to be the reserve of supercomputers. Unmanned Combat Aerial Vehicles (UCAVs) have made great strides in recent years, going from items of speculation to the decks of aircraft carriers. But however well they've done in taking off, landing, and carrying out assigned aerial missions, there's still been a big gap between what a human pilot can do and what a combat drone can hope to achieve. Until recently, experienced humans have found it easy to beat UCAVs in simulations after learning their tricks and weaknesses.