Waris Radji

RFIG achieves competitive performance in deep RL exploration while providing robust theoretical guarantees, challenging the reliance on complex neural network uncertainty estimations.

Dri-MED adapts to user preferences and context drifts, achieving significantly lower regret than traditional methods in dynamic environments.