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arXiv · 2006.15134

Critic Regularized Regression

Ziyu Wang·Alexander Novikov·Konrad Zolna·Jost Tobias Springenberg·Scott Reed·Bobak Shahriari·Noah Siegel·Josh Merel·Caglar Gulcehre·Nicolas Heess·Nando de Freitas
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Abstract

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.

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Ziyu Wang, Alexander Novikov, Konrad Zolna, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Josh Merel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas. 2020-06-26. Critic Regularized Regression. https://arxiv.org/abs/2006.15134

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