  We introduce learning and planning algorithms for average-reward MDPs,
including 1) the first general proven-convergent off-policy model-free control
algorithm without reference states, 2) the first proven-convergent off-policy
model-free prediction algorithm, and 3) the first off-policy learning algorithm
that converges to the actual value function rather than to the value function
plus an offset. All of our algorithms are based on using the
temporal-difference error rather than the conventional error when updating the
estimate of the average reward. Our proof techniques are a slight
generalization of those by Abounadi, Bertsekas, and Borkar (2001). In
experiments with an Access-Control Queuing Task, we show some of the
difficulties that can arise when using methods that rely on reference states
and argue that our new algorithms can be significantly easier to use.
