Search and rescue is often a slow process, which puts people at risk of being trapped, stranded or even worse, killed during natural disasters, such as earthquakes, floods and hurricanes. In order to provide better rescue assistance and to achieve high survival rates, we need efficient, cost effective and small crawling robots to execute search and rescue operations during disaster situations, especially in reaching spaces that are inaccessible for larger robots or are harmful to rescuers. Thus, I worked on improving the walking speed and autonomous behaviour of OctoRoACH, an inexpensive and robust palm-sized eight legged robot developed by the Biomimetic Millisystems Lab, together with my capstone project members and advisors at UC Berkeley. Our results show that reinforcement learning algorithms is useful to improve the walking speed of existing search and rescue robots across different terrains and save more lives during disaster situations.