DIfferent learning rates converging to same minima
This is the expected behavior. Different learning rates should converge to the same minimum if you are starting at the same location. 

If you're optimizing a neural network and you want to explore the loss surface, randomize the starting parameters. If you always start your optimization algorithm from the same initial value, you will reach the same local extremum unless you really increase the step size and overshoot.