Q($λ$) with Off-Policy Corrections

16 Feb 2016Anna HarutyunyanMarc G. BellemareTom StepletonRemi Munos

We propose and analyze an alternate approach to off-policy multi-step temporal difference learning, in which off-policy returns are corrected with the current Q-function in terms of rewards, rather than with the target policy in terms of transition probabilities. We prove that such approximate corrections are sufficient for off-policy convergence both in policy evaluation and control, provided certain conditions... (read more)

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