In this paper, we aim to develop a simple and scalable reinforcement learning
algorithm that uses standard supervised learning methods as subroutines. Our
goal is an algorithm that utilizes only simple and convergent maximum
likelihood loss functions, while also being able to leverage off-policy data.
Our proposed approach, which we refer to as advantage-weighted regression
(AWR), consists of two standard supervised learning steps: one to regress onto
target values for a value function, and another to regress onto weighted target
actions for the policy. The method is simple and general, can accommodate
continuous and discrete actions, and can be implemented in just a few lines of
code on top of standard supervised learning methods. We provide a theoretical
motivation for AWR and analyze its properties when incorporating off-policy
data from experience replay. We evaluate AWR on a suite of standard OpenAI Gym
benchmark tasks, and show that it achieves competitive performance compared to
a number of well-established state-of-the-art RL algorithms. AWR is also able
to acquire more effective policies than most off-policy algorithms when
learning from purely static datasets with no additional environmental
interactions. Furthermore, we demonstrate our algorithm on challenging
continuous control tasks with highly complex simulated characters.

该论文旨在开发一种简单且可扩展的增强学习算法，使用标准的监督学习方法作为子程序。提出的 AWR 方法只需几行代码即可实现，能够适应连续和离散行动，其性能与许多最先进的 RL 算法相当，无需其他环境交互即可从纯静态数据集中获取更有效的策略。