SAFE AND EFFICIENT REINFORCEMENT LEARNING

Sammanfattning: Pre-programming a robot may be efficient to some extent, but since a human has code the robot it will only be as efficient as the programming. The problem can solved by using machine learning, which lets the robot learn the most efficient way by itself. This thesis is continuation of a previous work that covered the development of the framework ​Safe-To-Explore-State-Spaces​ (STESS) for safe robot manipulation. This thesis evaluates the efficiency of the ​Q-Learning with normalized advantage function ​ (NAF), a deep reinforcement learning algorithm, when integrated with the safety framework STESS. It does this by performing a 2D task where the robot moves the tooltip on a plane from point A to point B in a set workspace. To test the viability different scenarios was presented to the robot. No obstacles, sphere obstacles and cylinder obstacles. The reinforcement learning algorithm only knew the starting position and the STESS pre-defined the workspace constraining the areas which the robot could not enter. By satisfying these constraints the robot could explore and learn the most efficient way to complete its task. The results show that in simulation the NAF-algorithm learns fast and efficient, while avoiding the obstacles without collision.