Solving CartPole in 8 Weights (cartpole.neocities.org)

A recent breakthrough in reinforcement learning has demonstrated the ability to solve the classic CartPole problem using just eight weights in a simple 2x4 matrix. This minimalist approach contrasts sharply with the complex neural networks typically employed to tackle such tasks. Instead of relying on multiple layers or intricate architectures, the solution directly correlates the four state variables—cart position, cart velocity, pole angle, and angular velocity—to two binary actions (push left or right). The efficacy of the method stems from the straightforward physics underlying CartPole, allowing the algorithm to encode balance directly through these eight coefficients. The significance of this achievement lies in its challenge to the notion that larger models equate to better performance in AI/ML. By employing the Cross-Entropy Method to iteratively refine the matrix, the approach emphasizes that sometimes elegance and simplicity are more powerful than complexity. Even when subjected to perturbations, the eight-weight controller performs robustly, driving the cart successfully under various conditions. This outcome not only showcases the potential for minimalistic designs in AI solutions but also serves as a reminder that true intelligence can emerge from remarkably simple constructs.