Adaptive Fighting Robot Training with Reinforcement Learning
The simultaneous process of balance and adversarial combat automation of an intrinsically unstable system—represented by an inverted pendulum mechanics model—has been successfully executed completely independent of any external model definitions (model-free) using a Deep Q-Network topology. A 4-phase design framework based on progressive difficulty calibration was executed, initiating from a baseline linear control (LQR) reference. Symmetric self-play competition across internal clones was executed to isolate and suppress overconfidence deviations emerging natively from single-axis optimization.