cpp-reinforcement-learning

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)

LibTorch provides the same tensor operations and autograd capabilities as PyTorch in C++.

Installation: Download from https://pytorch.org/get-started/locally (select C++/LibTorch)

CMake Integration:

cmake_minimum_required(VERSION 3.18)
project(rl_project)

set(CMAKE_CXX_STANDARD 17)
find_package(Torch REQUIRED)

add_executable(train_agent src/main.cpp)
target_link_libraries(train_agent "${TORCH_LIBRARIES}")

Secondary Libraries

• ONNX Runtime - Cross-platform inference deployment
• cpprl (mhubii/cpprl) - Reference PPO implementation
• Gymnasium C++ bindings - Environment interfaces

Quick Start: DQN Agent

#include <torch/torch.h>

struct DQNNet : torch::nn::Module {
    torch::nn::Linear fc1{nullptr}, fc2{nullptr}, fc3{nullptr};

    DQNNet(int64_t state_dim, int64_t action_dim) {
        fc1 = register_module("fc1", torch::nn::Linear(state_dim, 128));
        fc2 = register_module("fc2", torch::nn::Linear(128, 128));
        fc3 = register_module("fc3", torch::nn::Linear(128, action_dim));
    }

    torch::Tensor forward(torch::Tensor x) {
        x = torch::relu(fc1->forward(x));
        x = torch::relu(fc2->forward(x));
        return fc3->forward(x);
    }
};

// Training loop
auto policy_net = std::make_shared<DQNNet>(state_dim, action_dim);
auto target_net = std::make_shared<DQNNet>(state_dim, action_dim);
torch::optim::Adam optimizer(policy_net->parameters(), lr);

// Compute loss
auto q_values = policy_net->forward(states).gather(1, actions);
// Tensor::max(dim) returns std::tuple<values, indices> in C++
auto next_q = std::get<0>(target_net->forward(next_states).max(1)).detach();
auto target = rewards + gamma * next_q * (1 - dones);
auto loss = torch::mse_loss(q_values.squeeze(), target);

// Backward pass
optimizer.zero_grad();
loss.backward();
optimizer.step();