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We present the first provably convergent two-timescale off-policyactor-critic algorithm (COF-PAC) with function approximation. Key to COF-PAC isthe introduction of a new critic, the emphasis critic, which is trained viaGradient Emphasis Learning (GEM), a novel combination of the key ideas ofGradient Temporal Difference Learning and Emphatic Temporal DifferenceLearning. With the help of the emphasis critic and the canonical value functioncritic, we show convergence for COF-PAC, where the critics are linear and theactor can be nonlinear.

Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation