Deconfounding Reinforcement Learning in Observational Settings

Abstract

We propose a general formulation for addressing reinforcement learning (RL) problems in settings with observational data. That is, we consider the problem of learning good policies solely from historical data in which unobserved factors (confounders) affect both observed actions and rewards. Our formulation allows us to extend a representative RL algorithm, the Actor-Critic method, to its deconfounding variant, with the methodology for this extension being easily applied to other RL algorithms. In addition to this, we develop a new benchmark for evaluating deconfounding RL algorithms by modifying the OpenAI Gym environments and the MNIST dataset. Using this benchmark, we demonstrate that the proposed algorithms are superior to traditional RL methods in confounded environments with observational data. To the best of our knowledge, this is the first time that confounders are taken into consideration for addressing full RL problems with observational data. Code is available at https://github.com/CausalRL/DRL.

30 pages

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• Provably Efficient Causal Reinforcement Learning with Confounded Observational Data
• Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning
• Counterfactual Data Augmentation using Locally Factored Dynamics
• Causal Influence Detection for Improving Efficiency in Reinforcement Learning
• Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
• Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
• Dynamic Causal Bayesian Optimization
• Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation
• Sequential Deconfounding for Causal Inference with Unobserved Confounders
• Causal World Models by Unsupervised Deconfounding of Physical Dynamics
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