Semiparametric Double Reinforcement Learning with Applications to Long-Term Causal Inference

The exploration of Double Reinforcement Learning (DRL) in the article aligns with ongoing research in the field, particularly in addressing complex challenges in visual reasoning and continual learning. For instance, the related work on PROPA emphasizes the need for process-level optimization in visual reasoning, which shares thematic ties with DRL's focus on policy value inference. Similarly, the PANDA study on exemplar-free continual learning highlights the importance of efficient methodologies, resonating with the efficiency gains achieved through the proposed semiparametric DRL approach. Together, these studies underscore a broader trend in AI research towards enhancing learning frameworks and methodologies.