Both challenges involve modeling long-term dependencies.

A/B testing is critical for modern technological companies to evaluate the effectiveness of newly developed products against standard baselines. This paper studies optimal designs that aim to maximize the amount of information obtained from online experiments to estimate treatment effects accurately.

Extensive experiments show that our method generates engaging manipulation results conforming to the transformations entailed in demonstrations.

Safe reinforcement learning (RL) is a promising approach for many real-world decision-making problems where ensuring safety is a critical necessity.