Object focused q-learning for autonomous agents

We present Object Focused Q-learning (OF-Q), a novel reinforcement learning algorithm that can offer exponential speed-ups over classic Q-learning on domains composed of independent objects. An OF-Q agent treats the state space as a collection of objects organized into different object classes. Our key contribution is a control policy that uses non-optimal Q-functions to estimate the risk of ignoring parts of the state space. We compare our algorithm to traditional Q-learning and previous arbitration algorithms in two domains, including a version of Space Invaders.