Geopol Update: It will take Years for India to Catch Up to Pakistan Now
Important Insights from Recent Geopolitical Developments
Topics: (1200 words)
- Kashmir: It will take Years for India to Catch Up to Pakistan
- Dogfights are Obsolete
- Tariffs Update: China and US Meeting in Switzerland
- Taiwan: U.S. “might have to ward off invasion by China”
1- Kashmir: It will take Years for India to Catch Up to Pakistan Now
Authored by Mavs for GoldFix,ZH Edit
As more details emerged from the air combat between India and Pakistan, military experts have taken a deep dive and found a lot of interesting facts, making the French sob and the West alert. Aside from the first battlefield showdown between the PLA and NATO equipment (plus 2 Russian jets), how the Pakistani achieved such success was more intriguing than the headline hypes.
In recent years, Russia, China and the US have shared a common strategic view when it comes to aerial combats though with different approaches – BVR (Beyond Visual Range) capabilities. All of the Indian jets were shot down in her own territory, including one drone (all BVR strikes). The PL-15 air-to-air missile used in this battle is believed to be the “export version” (much shorter range) hence the use of data link and the superior air combat command system of Pakistan are the scary part, and likely why both sides agreed to a truce (let Trump take this credit, nobody cares) because India (and the US) knew it would be pointless to mobilize the army when your air force can’t even pose a threat.
2- BVR Makes Dogfights Obsolete
A lot of critical details were intentionally omitted from the press conference held by the Pakistani Air Force. For obvious reasons we cannot get into details here, but we can highlight the mechanisms and players involved.
Before doing so:, here is an excerpt from the paper describing the approach Pakistan used titled Beyond-Visual-Range Air Combat Tactics
Abstract—For quite a long time, effective Beyond-Visual-Range (BVR) air combat tactics can only be discovered by human pilots in the actual combat process. However, due to the lack of actual combat opportunities, making new air combat tactics innovation was generally considered quite difficult. To address this challenge, we first introduced a solely end-to-end Reinforcement Learning (RL) approach for training competitive air combat agents with adversarial self-play from scratch in a high fidelity air combat simulation environment during training. Furthermore, a Key Air Combat Event Reward Shaping (KAERS) mechanism was proposed to provide sparse but objective shaped rewards beyond episodic win/lose signal to accelerate the initial machine learning process. Experimental results showed that multiple valuable air combat tactical behaviors emerged progressively. We hope this study could be extended to the future of air combat machine intelligence research.
