Deep Learning Research Project

SonexAI

The First Autonomous Valorant AI Agent

A groundbreaking deep learning project built in C++ that creates an AI capable of playing Valorant autonomously - understanding game state, communicating with teammates, and making real-time tactical decisions.

1.2M+

Training Matches

C++

Core Engine

Neural Layers

v1.0

Current Version

Core Capabilities

How SonexAI Works

A modular AI system combining computer vision, reinforcement learning, and natural language processing to create an autonomous Valorant agent.

Autonomous Gameplay

The AI perceives the game environment through computer vision, processes game state, and executes actions without human input - agent selection, movement, aim, and ability usage.

Natural Language Communication

Integrated NLP module enables real-time voice and text communication with human teammates - giving callouts, requesting support, and coordinating strategies.

Computer Vision Pipeline

Real-time frame analysis at 144+ FPS using custom CNN architectures for enemy detection, map awareness, and object recognition specific to Valorant.

Deep Reinforcement Learning

Trained using PPO and custom reward shaping on 1.2M+ match replays. The model learns optimal policies for combat, economy management, and team coordination.

Strategic Decision Engine

Monte Carlo Tree Search combined with neural evaluation for high-level strategy - site executes, rotations, ability timing, and economic decisions.

Low-Latency Execution

Custom C++ inference engine with SIMD optimizations achieves sub-10ms decision latency. Actions are executed with human-like timing and mouse movement patterns.

Architecture

Pure C++ Performance

SonexAI's inference engine is written entirely in C++ for maximum performance. The neural network runs on a custom framework optimized for real-time gaming, achieving sub-10ms decision latency.

Custom Tensor Library

Hand-optimized matrix operations with AVX-512 SIMD instructions for neural network inference

Vision Pipeline

GPU-accelerated frame capture and preprocessing at 144+ FPS with DirectX hooks

Action System

Smooth mouse movement interpolation and input timing that mimics human behavior

Memory Efficient

Entire model weights and inference fit within 512MB RAM footprint

valorant_agent.cpp

 1// SonexAI Neural Engine - Core Architecture
 2namespace sonex::neural {
 3
 4class ValorantAgent {
 5private:
 6    VisionEncoder vision_encoder_;
 7    PolicyNetwork policy_net_;
 8    ValueNetwork value_net_;
 9    ActionDecoder action_decoder_;
10    
11public:
12    Action step(const Frame& frame) {
13        // Extract visual features
14        auto features = vision_encoder_.encode(frame);
15        
16        // Get policy distribution
17        auto policy = policy_net_.forward(features);
18        
19        // Sample action from policy
20        auto action = action_decoder_.decode(policy);
21        
22        return action;
23    }
24};
25
26} // namespace sonex::neural

Neural Network

Model Architecture

A 47-layer deep neural network combining convolutional vision encoders with transformer-based decision making.

Input Layer

Game Frame (1920x1080 RGB)

Vision Encoder

ResNet-50 Backbone

Feature Extraction

Multi-head Attention

Policy Head

Action Distribution

Value Head

State Evaluation

Output Layer

Action + Confidence

1.2M+

Training Data

Match replays from Iron to Radiant

127M

Model Parameters

Optimized for real-time inference

2,400

Training Time

GPU hours on A100 cluster

73%

Win Rate

Against human opponents

Training Pipeline

Data Collection

Gather match replays from all skill levels with full game state

Preprocessing

Extract frames, actions, rewards, and game state features

Training

PPO with custom reward shaping on distributed GPU cluster

Evaluation

Test against human players and benchmark performance