AI Dogfight Simulation
Led development of deep reinforcement learning systems for autonomous flight combat simulations, achieving 85%+ win rate against human pilots in dogfight scenarios.
85%+
Win Rate vs Humans
5+
Engineers Led
60Hz
Control Frequency
<5ms
Inference Latency
The Challenge
Training fighter pilots is extraordinarily expensive and risky. Simulation-based training helps, but current AI opponents follow predictable patterns that experienced pilots quickly learn to exploit. The defense industry needed AI adversaries that could adapt, learn, and challenge human pilots in realistic combat scenarios.
Key Challenges
- •Creating AI that exhibits unpredictable, human-like tactical behavior
- •Handling the high-dimensional continuous action space of aircraft control
- •Ensuring real-time decision making at 60+ Hz control frequency
- •Developing AI that can generalize across different aircraft and scenarios
- •Balancing between aggressive tactics and survivability
The Solution
I led a team of 5 engineers to develop a multi-agent deep reinforcement learning system using hierarchical policies. The high-level tactical agent makes strategic decisions (engage, evade, pursue), while low-level controllers handle aircraft maneuvering. This separation allowed for both sophisticated tactics and precise control.
Implemented Soft Actor-Critic (SAC) algorithm for continuous control with entropy regularization
Designed hierarchical policy architecture separating tactical decisions from flight control
Built custom simulation environment with realistic flight dynamics and weapon systems
Developed curriculum learning pipeline progressing from basic maneuvers to full combat
Created self-play training regime for continuous improvement against evolving opponents
System Architecture
The system uses a hierarchical multi-agent architecture with centralized training and decentralized execution, enabling complex tactical behavior while maintaining real-time performance.
Tactical Decision Network
Transformer-based policy network processing situational awareness data. Outputs high-level tactical primitives (attack, defend, disengage) with associated parameters.
Maneuver Controller
MLP-based low-level controller translating tactical commands into aircraft control inputs (pitch, roll, yaw, throttle). Trained via imitation learning on expert trajectories.
Situational Awareness Module
Processes sensor data (radar, RWR, visual) into a unified state representation. Handles uncertainty and sensor limitations realistically.
Opponent Modeling
Online learning component that builds models of opponent behavior during engagement. Enables adaptive counter-tactics.
Training Infrastructure
Distributed training across 64 GPU workers with synchronized parameter updates. Custom simulation parallelization for high throughput.
Key Implementation Details
Hierarchical Policy Design
The two-level hierarchy was crucial for success. The tactical network operates at 2Hz, making strategic decisions based on the big picture. The maneuver controller runs at 60Hz, executing precise aircraft control. This separation mirrors how human pilots think—strategy at one level, reflexive flying at another.
Curriculum Learning for Combat
Training an agent from scratch on full combat is impractical—the reward signal is too sparse. I designed a curriculum starting with basic flight (maintain altitude, heading), progressing to pursuit (track target), then to weapons employment (valid firing solutions), and finally full engagement. Each stage bootstrapped from the previous.
Self-Play with Diversity
Pure self-play tends to converge to narrow strategies. I introduced population-based training where multiple agent variants train simultaneously, maintaining strategic diversity. A league system ensured agents couldn't over-specialize against specific opponents.
Real-Time Inference Optimization
Combat requires split-second decisions. I optimized the inference pipeline using ONNX Runtime with TensorRT backend, achieving sub-5ms inference latency. Careful attention to memory allocation eliminated garbage collection pauses during combat.
Tech Stack
Reinforcement Learning
Simulation
Optimization
Infrastructure
Key Learnings
Hierarchical policies are essential for complex control problems with multiple time scales
Curriculum learning transforms impossible problems into tractable ones
Self-play requires careful diversity maintenance to avoid strategy collapse
Defense applications demand rigorous testing—AI must fail gracefully when uncertain
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