Thibaud Rahier

Traditional one-point feedback in bandit problems misses the mark, but new algorithms can exploit action similarities to achieve significantly lower regret.

Unlock $\sqrt{N}$ regret in offline policy learning, even with complex policy classes, by trading off policy and environment complexity.