  Reinforcement learning is a popular machine learning paradigm which can find
near optimal solutions to complex problems. Most often, these procedures
involve function approximation using neural networks with gradient based
updates to optimise weights for the problem being considered. While this common
approach generally works well, there are other update mechanisms which are
largely unexplored in reinforcement learning. One such mechanism is Extreme
Learning Machines. These were initially proposed to drastically improve the
training speed of neural networks and have since seen many applications. Here
we attempt to apply extreme learning machines to a reinforcement learning
problem in the same manner as gradient based updates. This new algorithm is
called Extreme Q-Learning Machine (EQLM). We compare its performance to a
typical Q-Network on the cart-pole task - a benchmark reinforcement learning
problem - and show EQLM has similar long-term learning performance to a
Q-Network.
