cpp-reinforcement-learning

name	cpp-reinforcement-learning
description	C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime

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);
auto next_q = target_net->forward(next_states).max(1).values.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();

Essential Patterns

Replay Buffer (Ring Buffer)

class ReplayBuffer {
public:
    explicit ReplayBuffer(size_t capacity)
        : capacity_(capacity), position_(0), size_(0) {
        buffer_.reserve(capacity);
    }

    void push(Experience exp) {
        if (buffer_.size() < capacity_) {
            buffer_.push_back(std::move(exp));
        } else {
            buffer_[position_] = std::move(exp);
        }
        position_ = (position_ + 1) % capacity_;
        size_ = std::min(size_ + 1, capacity_);
    }

    std::vector<Experience> sample(size_t batch_size);

private:
    std::vector<Experience> buffer_;
    size_t capacity_, position_, size_;
    std::mt19937 rng_{std::random_device{}()};
};

GPU Device Management

torch::Device device = torch::cuda::is_available() ? torch::kCUDA : torch::kCPU;
model->to(device);

// Create tensors on device
auto tensor = torch::zeros({batch_size, state_dim},
    torch::TensorOptions().device(device).dtype(torch::kFloat32));

Inference Mode

{
    torch::NoGradGuard no_grad;
    auto action_values = model->forward(state);
    auto action = action_values.argmax(1);
}

Common Pitfalls

1. Forgetting train/eval mode - Call model->train() or model->eval()
2. Missing NoGradGuard - Use for inference to save memory
3. Tensor accumulation - Use .detach() for stored tensors
4. Thread safety - Clone models for parallel threads
5. Device mismatch - Verify all tensors on same device

Reference Files

• references/libtorch.md - LibTorch setup and API guide
• references/algorithms.md - DQN, PPO, SAC implementations
• references/memory-management.md - Replay buffers, smart pointers, RAII
• references/performance.md - Optimization, parallelization, GPU
• references/testing.md - Testing and debugging strategies