Safe and Efficient Off-Policy Reinforcement Learning. (arXiv:1606.02647v1 [cs.LG])

In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace($\lambda$), with three desired properties: (1) low variance; (2) safety, as it safely uses samples collected from any behaviour policy, whatever its degree of "off-policyness"; and (3) efficiency, as it makes the best use of samples collected from near on-policy behaviour policies. We analyse the contractive nature of the related operator under both off-policy policy evaluation and control settings and derive online sample-based algorithms. To our knowledge, this is the first return-based off-policy control algorithm converging a.s. to $Q^*$ without the GLIE assumption (Greedy in the Limit with Infinite Exploration). As a corollary, we prove the convergence of Watkins' Q($\lambda$), which was still an open problem. We illustrate the benefits of Retrace($\lambda$) on a standard suite of Atari 2600 games.

from cs.AI updates on arXiv.org http://ift.tt/1U86Uis
via IFTTT