This paper presents application of Banditron - an online reinforcement learning algorithm (RL) in a discrete state intra-cortical Brain Machine Interface (iBMI) setting. We have analyzed two datasets from non-human primates (NHPs) - NHP A and NHP B each performing a 4-option discrete control task over a total of 8 days. Results show average improvements of≈10%,6%in NHP A and 27%,13%in NHP B overstate of the art algorithms - Hebbian Reinforcement Learning (HRL) and Attention Gated Reinforcement Learning (AGREL).