Marginalized Operators for Off-policy Reinforcement Learning

Yunhao Tang; Mark Rowland; Rémi Munos; Michal Valko

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arXiv · 2203.16177

Marginalized Operators for Off-policy Reinforcement Learning

Yunhao Tang·Mark Rowland·Rémi Munos·Michal Valko

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Abstract

In this work, we propose marginalized operators, a new class of off-policy evaluation operators for reinforcement learning. Marginalized operators strictly generalize generic multi-step operators, such as Retrace, as special cases. Marginalized operators also suggest a form of sample-based estimates with potential variance reduction, compared to sample-based estimates of the original multi-step operators. We show that the estimates for marginalized operators can be computed in a scalable way, which also generalizes prior results on marginalized importance sampling as special cases. Finally, we empirically demonstrate that marginalized operators provide performance gains to off-policy evaluation and downstream policy optimization algorithms.

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Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko. 2022-03-30. Marginalized Operators for Off-policy Reinforcement Learning. https://arxiv.org/abs/2203.16177

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