All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL
ARAYA, Inc.（ARAYA公司）；Imperial College London（伦敦帝国学院）；The Swiss AI Lab IDSIA（瑞士人工智能实验室IDSIA）；Università della Svizzera Italiana (USI)（瑞士意大利语区大学）；Scuola Universitaria Professionale della Svizzera Italiana (SUPSI)（瑞士意大利语区应用科学大学）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；NNAISENSE（NNAISENSE公司）
英文摘要：
Upside down reinforcement learning (UDRL) flips the conventional use of the return in the objective function in RL upside down, by taking returns as input and predicting actions. UDRL is based purely on supervised learning, and bypasses some prominent issues in RL: bootstrapping, off-policy corrections, and discount factors. While previous work with UDRL demonstrated it in a traditional online RL setting, here we show that this single algorithm can also work in the imitation learning and offline RL settings, be extended to the goal-conditioned RL setting, and even the meta-RL setting. With a general agent architecture, a single UDRL agent can learn across all paradigms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
