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 implemented with a model-free reinforcement-learning approach using a Deep Q-Network. A 4-phase design framework based on progressive difficulty calibration was executed.