迈向可部署的RL --RL研究的缺陷和潜在的修复
Towards Deployable RL -- What's Broken with RL Research and a Potential Fix
Technion（以色列理工学院）；Nvidia Research（英伟达研究院）
摘要：
强化学习（RL）已经展示了巨大的潜力，但目前充满了过度炒作和白日梦。我们指出目前研究的一些困难，我们认为这是社会所采取的方向特有的。对我们来说，目前的方向不太可能导致“可部署”的RL：RL在实践中工作，可以在实际情况下工作，但仍然是经济上可行的。我们还提出了一个潜在的修复该领域的一些困难。
英文摘要：
Reinforcement learning (RL) has demonstrated great potential, but is currently full of overhyping and pipe dreams. We point to some difficulties with current research which we feel are endemic to the direction taken by the community. To us, the current direction is not likely to lead to "deployable" RL: RL that works in practice and can work in practical situations yet still is economically viable. We also propose a potential fix to some of the difficulties of the field.
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。
