  We pursue a life-long learning approach to artificial intelligence that makes
extensive use of reinforcement learning algorithms. We build on our prior work
with general value functions (GVFs) and the Horde architecture. GVFs have been
shown able to represent a wide variety of facts about the world's dynamics that
may be useful to a long-lived agent (Sutton et al. 2011). We have also
previously shown scaling - that thousands of on-policy GVFs can be learned
accurately in real-time on a mobile robot (Modayil, White & Sutton 2011). That
work was limited in that it learned about only one policy at a time, whereas
the greatest potential benefits of life-long learning come from learning about
many policies in parallel, as we explore in this paper. Many new challenges
arise in this off-policy learning setting. To deal with convergence and
efficiency challenges, we utilize the recently introduced GTD({\lambda})
algorithm. We show that GTD({\lambda}) with tile coding can simultaneously
learn hundreds of predictions for five simple target policies while following a
single random behavior policy, assessing accuracy with interspersed on-policy
tests. To escape the need for the tests, which preclude further scaling, we
introduce and empirically vali- date two online estimators of the off-policy
objective (MSPBE). Finally, we use the more efficient of the two estimators to
demonstrate off-policy learning at scale - the learning of value functions for
one thousand policies in real time on a physical robot. This ability
constitutes a significant step towards scaling life-long off-policy learning.
