Confounding Robust Deep Reinforcement Learning: A Causal Approach

Mingxuan Li, Junzhe Zhang, Elias Bareinboim

Keywords: Causal Inference, Reinforcement Learning, Deep Learning

TL;DR: This paper proposes a novel deep reinforcement learning algorithm robust to unobserved confounding bias in observed data over complex and high-dimensional domains.

Abstract: A key task in Artificial Intelligence is learning effective policies for controlling agents in unknown environments to optimize performance measures. Off-policy learning methods, like Q-learning, allow learners to make optimal decisions based on past experiences. This paper studies off-policy learning from biased data in complex and high-dimensional domains where \emph{unobserved confounding} cannot be ruled out a priori. Building on the well-celebrated Deep Q-Network (DQN), we propose a novel deep reinforcement learning algorithm robust to confounding biases in observed data. Specifically, our algorithm attempts to find a safe policy for the worst-case environment compatible with the observations. We apply our method to twelve confounded Atari games, and find that it consistently dominates the standard DQN in all games where the observed input to the behavioral and target policies mismatch and unobserved confounders exist.

Primary Area: Probabilistic methods (e.g., variational inference, causal inference, Gaussian processes)

Submission Number: 18850