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Surrogate Objectives for Batch Policy Optimization in One-step Decision Making
Minmin Chen · Ramki Gummadi · Chris Harris · Dale Schuurmans

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #215

We investigate batch policy optimization for cost-sensitive classification and contextual bandits---two related tasks that obviate exploration but require generalizing from observed rewards to action selections in unseen contexts. When rewards are fully observed, we show that the expected reward objective exhibits suboptimal plateaus and exponentially many local optima in the worst case. To overcome the poor landscape, we develop a convex surrogate that is calibrated with respect to entropy regularized expected reward. We then consider the partially observed case, where rewards are recorded for only a subset of actions. Here we generalize the surrogate to partially observed data, and uncover novel objectives for batch contextual bandit training. We find that surrogate objectives remain provably sound in this setting and empirically demonstrate state-of-the-art performance.

Author Information

Minmin Chen (Google)
Ramki Gummadi (Google)
Chris Harris (Google)
Dale Schuurmans (Google Brain & University of Alberta)

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