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Deep reinforcement learning (RL) methods have achieved remarkable performance on challenging control tasks. Observations of the resulting behavior give the impression that the agent has constructed a generalized representation that supports insightful action decisions. We re‐examine what is meant by generalization in RL, and propose several definitions based on an agent's performance in on‐policy, off‐policy, and unreachable states. We propose a set of practical methods for evaluating agents with these definitions of generalization. We demonstrate these techniques on a common benchmark task for deep RL, and we show that the learned networks make poor decisions for states that differ only slightly from on‐policy states, even though those states are not selected adversarially. We focus our analyses on the deep Q‐networks (DQNs) that kicked off the modern era of deep RL. Taken together, these results call into question the extent to which DQNs learn generalized representations, and suggest that more experimentation and analysis is necessary before claims of representation learning can be supported.

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Causal inference can be formalized as Bayesian inference that combines a prior distribution over causal models and likelihoods that account for both observations and interventions. We show that it is possible to implement this approach using a sufficiently expressive probabilistic programming language. Priors are represented using probabilistic programs that generate source code in a domain specific language. Interventions are represented using probabilistic programs that edit this source code to modify the original generative process. This approach makes it straightforward to incorporate data from atomic interventions, as well as shift interventions, variance-scaling interventions, and other interventions that modify causal structure. This approach also enables the use of general-purpose inference machinery for probabilistic programs to infer probable causal structures and parameters from data. This abstract describes a prototype of this approach in the Gen probabilistic programming language. * Contributed equally.Preprint. Under review.