Critic Regularized Regression

06/26/2020 ∙ by Ziyu Wang, et al. ∙ 32

Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction. It addresses challenges with regard to the cost of data collection and safety, both of which are particularly pertinent to real-world applications of RL. Unfortunately, most off-policy algorithms perform poorly when learning from a fixed dataset. In this paper, we propose a novel offline RL algorithm to learn policies from data using a form of critic-regularized regression (CRR). We find that CRR performs surprisingly well and scales to tasks with high-dimensional state and action spaces – outperforming several state-of-the-art offline RL algorithms by a significant margin on a wide range of benchmark tasks.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 5

page 18

page 19

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.