High-level machine learning libraries like PyTorch and TensorFlow make training intelligent agents straightforward, but relying on modern frameworks often hides the complex mechanics working under the hood.
We just posted a video course on the freeCodeCamp.org YouTube channel that demonstrates how to build an entire reinforcement learning framework from scratch in standard C without relying on external libraries or third-party engines.
Here is what the course covers:
Implement custom automatic differentiation alongside dynamic computational graphs, memory allocation systems, and matrix data structures to handle forward and backward gradient passes.
Code fundamental linear algebra operations from the ground up, including transposed matrix multiplications, ReLU activations, and numerically stable Softmax routines.
Develop a fully functional Snake game simulation in C, complete with custom state vector encoding, grid navigation, collision detection, and reward assignment mechanics.
Build an end-to-end policy gradient pipeline utilizing trajectory rollouts, replay buffers, advantage estimation, and parameter updates to train the agent autonomously.
Writing reinforcement learning algorithms in C strips away high-level abstractions, forcing a precise understanding of memory layout, computational complexity, and the mathematical foundations of backpropagation. By seeing how every node in a computational graph is allocated and traversed, you gain a deeper, intuitive grasp of how modern neural network architectures execute beneath their high-level wrappers.
This course provides a practical, step-by-step guide in building AI systems from first principles. Watch the full course on the freeCodeCamp.org YouTube channel (2-hour watch).