Generalization across environments is critical to the successful application
of reinforcement learning algorithms to real-world challenges. In this paper,
we consider the problem of learning abstractions that generalize in block MDPs,
families of environments with a shared latent state space and dynamics
structure over that latent space, but varying observations. We leverage tools
from causal inference to propose a method of invariant prediction to learn
model-irrelevance state abstractions (MISA) that generalize to novel
observations in the multi-environment setting. We prove that for certain
classes of environments, this approach outputs with high probability a state
abstraction corresponding to the causal feature set with respect to the return.
We further provide more general bounds on model error and generalization error
in the multi-environment setting, in the process showing a connection between
causal variable selection and the state abstraction framework for MDPs. We give
empirical evidence that our methods work in both linear and nonlinear settings,
attaining improved generalization over single- and multi-task baselines.Comment: Accepted to ICML 2020. 16 pages, 8 figure