We demonstrate how agents robust to domain shifts can infer the causal model of the environment in mediated tasks, multi-agent settings and sequential decision tasks.

We solve POMDPs by nesting sequential Monte Carlo

Meta-RL with self-supervised predictive coding modules can learn interpretable, task-relevant representations that better approximate Bayes-optimal belief states than black-box meta-RL models across diverse partially observable environments.