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
- Forgetting train/eval mode - Call
model->train()ormodel->eval() - Missing NoGradGuard - Use for inference to save memory
- Tensor accumulation - Use
.detach()for stored tensors - Thread safety - Clone models for parallel threads
- 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