  In this paper, a comparison of reinforcement learning algorithms and their
performance on a robot box pushing task is provided. The robot box pushing
problem is structured as both a single-agent problem and also a multi-agent
problem. A Q-learning algorithm is applied to the single-agent box pushing
problem, and three different Q-learning algorithms are applied to the
multi-agent box pushing problem. Both sets of algorithms are applied on a
dynamic environment that is comprised of static objects, a static goal
location, a dynamic box location, and dynamic agent positions. A simulation
environment is developed to test the four algorithms, and their performance is
compared through graphical explanations of test results. The comparison shows
that the newly applied reinforcement algorithm out-performs the previously
applied algorithms on the robot box pushing problem in a dynamic environment.
