Reinforcement learning, in a nutshell, is a form of learning that enables the robot to construct a control law by a system of feedback signals that reinforce electrical path ways that produce correct response, and conversely wipe-out connections that produce errors. Unfortunately, without biasing, it is a weak learning that presents unreasonable difficulty, especially when it is applied to real robots. The subject of this thesis is to study, for a particular class of problems, the effects of different form of biases on the speed of learning as well as on the quality of final learned policy, and to realize this learning paradigm on a physical robot by appropriately biasing the robot with domain knowledge that determines how much the robot knows about the different parts of its world.