Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data

Sunil Madhow, Dan Qiao, Ming Yin +1

Developing theoretical guarantees on the sample complexity of offline RL methods is an important step towards making data-hungry RL algorithms practically viable. Currently, most r…