C++ Reinforcement Learning

Overview

This skill covers implementing reinforcement learning algorithms in C++ using LibTorch (PyTorch C++ frontend) and modern C++17/20 features. It provides patterns for building high-performance RL systems suitable for production deployment, robotics, game AI, and real-time applications.

When to Use

• Implementing DQN, PPO, SAC, or other RL algorithms in C++
• Building performance-critical RL training pipelines
• Creating efficient replay buffers with proper memory management
• Deploying trained models with ONNX Runtime
• Parallelizing environment rollouts across threads
• Integrating RL with existing C++ codebases (games, robotics, simulations)

Core Libraries

Primary: LibTorch (PyTorch C++ Frontend)