We investigate adaptive minimal routing in 2D torus networks on chip NoCs under node fault conditions comparing a reinforcement learning RL based strategy to an adaptive routing baseline A torus topology is used for its low diameter high connectivity properties The RL approach models each router as an agent that learns to forward packets based on network state while the adaptive scheme uses fixed minimal paths with simple rerouting around faults We implement both methods in simulation injecting up to 50 node faults uniformly at random Key metrics are measured 1 throughput vs offered load at fault density 02 2 packet delivery ratio PDR vs fault density and 3 a fault adaptive score FT vs fault density Experimental results show the RL method achieves significantly higher throughput at high load approximately 2030 gain and maintains higher reliability under increasing faults The RL router delivers more packets per cycle and adapts to faults by exploiting path diversity whereas the adaptive scheme degrades sharply as faults accumulate In particular the RL approach preserves end to end connectivity longer PDR remains above 90 until approximately 3040 faults while adaptive PDR drops to approximately 70 at the same point The fault adaptive score likewise favors RL routing Thus RL based adaptive routing demonstrates clear advantages in throughput and fault resilience for torus NoCs